Care from the patients perspective - diabetes Essay

Care from the patients perspective - diabetes - Essay Example When there is inadequate insulin glucose cannot enter their body’s cells, and so builds up in their blood and is unable to be used as fuel. In some cases, the patient’s body is producing enough insulin but it is not working properly. This is known as insulin resistance. The two most common types of the condition are Type 1 diabetes and Type 2 diabetes. Diabetes is a complicated disease that medical professionals and scientists are just beginning to understand in terms of its causes. Although symptoms and treatment options are more clear, there are also a multiplicity of these, as well as different types of diabetes. The more common type is Type 2, so the current report focuses mainly on this type. Basically what both types share, in that they are related, is a defining feature of diabetes itself: in cases of diabetes, the body cannot handle or regulate its own blood glucose levels. Glucose, which is given to the body in the form of sugar, is what causes the body to have energy required for tasks of everyday life. This report looks primarily at the causes of diabetes as well as some common treatments today, with a focus on developing more patient-centered care in the UK to address this growing problem, and provide more respect for unique needs of minorities. Patient needs In terms of person and health, the core concepts of healthcare work, these are very interrelated. To me, person means centering on the client. Patient-centered communication is the key to social work implementation and focus, but there are healthcare settings that have different policies. In a client-oriented method, a facility could have different types of patients whose needs differ. This is an increasingly salient option in a setting in which patient attenuation has become a watch-word, and patient attenuation is another facet of the modern healthcare organization that tends to unify rather than divide care priorities. For example, accounting for patient needs it is a major par t of safety at healthcare institutions. Uniting concepts of person and health, one can look at how resistance can particularly impact care of facility residents, by examining morbidity and mortality rates. Diabetes mellitus exists when a patient has a deficiency of insulin or the resistance to insulin in their system, and it may result in symptoms such as an inordinate amount of urination and the patient’s being constantly thirsty as well as other problems. These symptoms are common to diabetes mellitus, but in the case of diabetes insipidus, another type of diabetes, there is no insulin deficiency. Changes in policy In healthcare in the UK generally, a shift towards patient-centered policy has occurred in recent years. To avoid resistance as counterproductive, a patient-centered approach is used, basically, that concentrates on the ways in which patients can help themselves by finding solutions that improve their health and construction of reality. This is a basically optimi stic assumption that object relation has as its impetus, in that the patients are expected to be cooperative and provide meaningful solutions that are assumedly more direct and experiential than an interruptive codified presentation that is staff-centered. In this method, the

Formative Assessment Essay Example for Free

Formative Assessment Essay The Rape of the Lock, written by Pope in response to a feud between two friends about the theft of a lock of hair, is revolutionary in its evolution of the comic satire genre into the field of epic poetry. Pope, an avid student of the Greek epics (he produced his own translations of some that provided much of his income during his life), takes the basic skeleton of an epic: its structure, critical content and even linguistic points; and crafts around the skeleton a poem of wit and comedy that is at its core epic, but also uses this very epic backbone to undermine its tales own importance and to satirise the content that has been moulded around the form. This creation from Pope marks the offshoot of the epic genre, transforming it into mock epic, an independent genre that bears many of the traits of its forebearer in a new light. The transformations to the epic that Pope undertakes in the Rape of the Lock to satiric effect can be broadly split into transformations of heroic content and transformations of heroic language. The former can be clearly observed here: Pope takes a staple of epic writing, heroic weaponry, and twists its use to his satirical needs. The weapon itself is given, through the use of a similar description, equal place with great weapons like Agamemnons sceptre, whose lineage was used to reinforce Agamemnons dominance and power in the Iliad, being forged by Hephaestus and owned by the Gods from Zeus to Kronos. Belindas weapons lineage is far less great. Instead of a scepter, the weapon of kings and priests in Homers writing, Belinda wields a bodkin, a hair needle. Even that difference itself is satirical: Agamemnons kingship is of great import to the Iliad so the parallel with a bodkin, which links to the hair in question much like the scepter links with kingship, makes a clear statement on the relative importance of the quarrel in the Rape of the Lock. The lineage too satirises the pointlessness of dispute: no claim of divinity (and thus righteousness) is made on the part of Belindas weapon; in fact its lineage mainly consists of feminine objects with the only male mentioned in its lineage also being the only one to explicitly be mentioned dying. Perhaps Pope, often accused of being somewhat sexist, is using this contrast and development to imply that the whole issue is a womans trifle and nothing next to the male quarrels of Achilles and Agamemnon. On top of this, the weapon is not the fixed centre of the lineage as in the Iliad, in which the weapon started as a divine weapon and stayed that way. Instead the object is mutable: it starts as signet rings, develops into a buckle and then becomes a bodkin. Pope changes up the epic formula of the mighty weapon into something changeable and thus insignificant, paralleling with the argument he is satirising, the implication being that it is insignificant and will easily be forgotten. The weapon also shows another perversion of the epic poem that Pope uses. Protection, be it through armour or weaponry, tends to have a high place in the Greek epics. Heroes often wear famed suits of armour or use shields/weapons to survive insurmountable odds (for example the reflective shield in Perseus tale in Ovids Metamorphoses that slays Medusa). This element of protection, divine or otherwise, is a theme that Pope subverts consistently. From the slyph Ariel who is half dissolvd even by light to the Cosmetic powers of her dress and make-up, nothing effectively protects Belinda. The bodkin is no different, it fails to protect her locks from being cut in the initial case, and here, although she uses it to attack the Baron, it fails to return its charge, her hair, to her. Pope is modernising traditional epics, using these typical protections to mock high societies fixation on appearance. All of her outward facing beauty and quaint bodkins cannot protect her from the advances of a single man, so what, Pope asks, is the use of all this artifice? Pope also mutates generic language elements from epic literature for his satirical intentions. In this passage, the clearest example is in his use of the ten syllable rhyming heroic couplet. Pope takes the rhyme of the couplet and uses it to link together two separate words or ideas, often to a comical effect. Here, in the lines, Nor feared the chief the unequal fight to try, Who sought no more than on his foe to die. Pope has the first line of the couplet set up the Barons bravery by expounding his fearlessness in fighting against Belinda in unequal combat (ironic in itself due to Belindas natural weakness compared to his manly strength referred to in the next couplet) before defeating the heroic xpectancy with a sexual pun; the phrase to die holding at the time a dual meaning referring to sexual climax, and often premature climax at that. His heroism is built up and destroyed within a couplet with the contrast of noble bravery and base desire providing a humorous and satirical twist on the typical heroism of the heroic couplet by suggesting that the drive behind the Barons actions is, at its deepest level, sexual, rather than noble or courtly.

Partitioning Methods to Improve Obsolescence Forecasting

Partitioning Methods to Improve Obsolescence Forecasting Amol Kulkarni Abstract Clustering is an unsupervised classification of observations or data items into groups or clusters. The problem of clustering has been addressed by many researchers in various disciplines, which serves to reflect its usefulness as one of the steps in exploratory data analysis. This paper presents an overview of partitioning methods, with a goal of providing useful advice and references to identifying the optimal number of cluster and provide a basic introduction to cluster validation techniques. The aim of clustering methods carried out in this paper is to present useful information which would aid in forecasting obsolescence. INRODUCTION There have been more inventions recorded in the past thirty years than all the rest of recorded humanity, and this pace hastens every month. As a result, the product life cycle has been decreasing rapidly, and the life cycle of products no longer fit together with the life cycle of their components. This issue is termed as obsolescence, wherein a component can no longer be obtained from its original manufacturer. Obsolescence can be broadly categorized into Planned and Unplanned obsolescence. Planned obsolescence can be considered as a business strategy, in which the obsolescence of a product is built into it from its conception. As Philip Kotler termed it Much so-called planned obsolescence is the working of the competitive and technological forces in a free society-forces that lead to ever-improving goods and services. On the other hand, unplanned obsolescence causes more harm to a burgeoning industry than good. This issue is more prevalent in the electronics industry; the procurem ent life-cycles for electronic components are significantly shorter than the manufacturing and support life-cycle. Therefore, it is highly important to implement and operate an active management of obsolescence to mitigate and avoid extreme costs [1]. One such product that has been plagued by threat of obsolescence is the digital camera. Ever-since the invention of smartphones there has been a huge dip in the digital camera sales, as can be seen from Figure 1. The decreasing price, the exponential rate at which the pixels and the resolution of the smart-phones improved can be termed as few of the factors that cannibalized the digital camera market. Figure 1 Worldwide Sales of Digital Cameras (2011-2016) [2] and Worldwide sale of cellphones on the right (2007-2016) [3] CLUSTERING Humans naturally use clustering to understand the world around them. The ability to group sets of objects based on similarities are fundamental to learning. Researchers have sought to capture these natural learning methods mathematically and this has birthed the clustering research. To help us solve problems at-least approximately as our brain, mathematically precise notation of clustering is important [4]. Clustering is a useful technique to explore natural groupings within multivariate data for a structure of natural groupings, also for feature extraction and summarizing. Clustering is also useful in identifying outliers, forming hypotheses concerning relationships. Clustering can be thought of as partitioning a given space into K groups i.e., à °Ã‚ Ã¢â‚¬ËœÃ¢â‚¬Å": à °Ã‚ Ã¢â‚¬ËœÃ¢â‚¬ ¹ à ¢Ã¢â‚¬  Ã¢â‚¬â„¢ {1, à ¢Ã¢â€š ¬Ã‚ ¦, K}. One method of carrying out this partitioning is to optimize some internal clustering criteria such as the distance between each observation within a c luster etc. While clustering plays an important role in data analysis and serves as a preprocessing step for a multitude of learning task, our primary interest lies in the ability of clusters to gain more information from the data to improve prediction accuracy. As clustering, can be thought of separating classes, it should help in classification task. The aim of clustering is to find useful groups of objects, usefulness being defined by the goals of the data analysis. Most clustering algorithms require us to know the number of clusters beforehand. However, there is no intuitive way of identifying the optimal number of clusters. Identifying optimal clustering is dependent on the methods used for measuring similarities, and the parameters used for partitioning, in general identifying the optimal number of clusters. Determining number of clusters is often an ad hoc decision based on prior knowledge, assumptions, and practical experience is very subjective. This paper performs k-means and k-medoids clustering to gain information from the data structure that could play an important role in predicting obsolescence. It also tries to address the issue of assessing cluster tendency, which is a first and foremost step while carrying out unsupervised machine learning process. Optimization of internal and external clustering criteria will be carried out to identify the optimal number of cluster. Cluster Validation will be carried out to identify the most suitable clustering algorithm. DATA CLEANING Missing value in a dataset is a common occurrence in real world problems. It is important to know how to handle missing data to reduce bias and to produce powerful models. Sometimes ignoring the missing data, biases the answers and potentially leads to incorrect conclusion. Rubin in [7] differentiated between three types of missing values in the dataset: Missing completely at random (MCAR): when cases with missing values can be thought of as a random sample of all the cases; MCAR occurs rarely in practice. Missing at random (MAR): when conditioned on all the data we have, any remaining missing value is completely random; that is, it does not depend on some missing variables. So, missing values can be modelled using the observed data. Then, we can use specialized missing data analysis methods on the available data to correct for the effects of missing values. Missing not at random (MNAR): when data is neither MCAR nor MAR. This is difficult to handle because it will require strong assumptions about the patterns of missing data. While in practice the use of complete case methods which drops the observations containing missing values is quite common, this method has the disadvantage that it is inefficient and potentially leads to bias. Initial approach was to visually explore each individual variable with the help of VIM. However, upon learning the limitations of filling in missing values through exploratory data analysis, this approach was abandoned in favor of multiple imputations. Joint Modelling (JM) and Fully Conditional Specification (FCS) are the two emerging general methods in imputing multivariate data. If multivariate distribution of the missing data is a reasonable assumption, then Joint Modelling which imputes data based on Markov Chain Monte Carlo techniques would be the best method. FCS specifies the multivariate imputation model on a variable-by-variable basis by a set of conditional densities, one for each incomplete variable. Starting from an initial imputation, FCS draws imputations by iterating over the conditional densities. A low number of iterations is often sufficient. FCS is attractive as an alternative to JM in cases where no suitable multivariate distribution can be found [8]. The Multiple imputations approach involves filling in missing values multiple times, creating multiple complete datasets. Because multiple imputations involve creating multiple predictions for each missing value, the analysis of data imputed multiple times take into account the uncertainty in the imputations and yield accurate standard errors. Multiple imputation techniques have been utilized to impute missing values in the dataset, primarily because it preserves the relation in the data and it also preserves uncertainty about these relations. This method is by no means perfect, it has its own complexities. The only complexity was having variables of different types (binary, unordered and continuous), thereby making the application of models, which assumed multivariate normal distribution- theoretically inappropriate. There are several complexities that surface listed in [8]. In order to address this issue It is convenient to specify imputation model separately for each column in th e data. This is called as chained equations wherein the specification occurs at a variable level, which is well understood by the user. The first task is to identify the variables to be included in the imputation process. This generally includes all the variables that will be used in the subsequent analysis irrespective of the presence of missing data, as well as variables that may be predictive of the missing data. There are three specific issues that often come up when selecting variables: (1) creating an imputation model that is more general than the analysis model, (2) imputing variables at the item level vs. the summary level, and (3) imputing variables that reflect raw scores vs. standardized scores. To help make a decision on these aspects, the distribution of the variables may help guide the decision. For example, if the raw scores of a continuous measure are more normally distributed than the corresponding standardized scores then using the raw scores in the imputation model, will likely better meet the assumptions of the linear regressions being used in the imputation process. The following image shows the missing values in the data-frame containing the information regarding digital camera. Figure 2 Missing Variables We can see that Effective Pixels has missing values for all its observations. After cross verifying it with the source website, the web scrapper was rewriting to correctly capture this variable from the website. The date variable was converted from a numeric to a date and this enabled the identification of errors in the observation for USB in the dataset. Two cameras that were released in 1994 1995 were shown to have USB 2.0, after searching online, it was found out that USB 2.0 was released in the year 2005 and USB 1.0 was released in the year 1996. As, most of the cameras before 1997 used PC-serial port a new level was introduced to the USB variable to indicate this. DATA DESCRIPTION The dataset containing the specification of the digital cameras was acquired using rvest -package [5] in R from the url provided in [6]. The structure of the data set is as shown in Appendix A. The data-frame contains 2199 observation and 55 variables. Appendix B contains the descriptive statistics of the quantitative variables in the data-frame. Figure 4 The Distribution of Body-Type in the dataset Observation: Most of the compact, Large SLR and ultracompact cameras are discontinued. Figure 5 Plot showing the status of Digital Cameras from 1994-2017 Observation: Most of the cameras released before 2007 have been discontinued however, we can see that few cameras announced between the period of 1996-2006 are still in production. Fewer new cameras have been announced after the year 2012, this can be evidenced due to the decreasing number of camera sales presented in Figure 5. Figure 6 Distribution of different Cameras (1994-2017) Observation: Between the period of 1996 2012 the digital camera market was dominated by the compact cameras. After 2012, fewer new compact cameras have been announced or are still in production. Same can be said about the fate of ultracompact cameras. In the year 2017, only SLR style mirrorless cameras have been announced, signaling the death of point and shoot cameras. Figure 7 Plot showing the Change in the Total Resolution and Effective Pixels of Digital Camera over the Years Observation: Total resolution has seen an improvement over the years. The presence of outliers can be seen in the top-left corner of the plot. Although the effective pixel is around 10, the total resolution is far higher than any of the cameras announced between the period 1996-2001. These could be the cameras that are still in production as evidenced from Figure 7. ASSESSING CLUSTER TENDENCY A primary issue with unsupervised machine learning is the fact if carried out blindly, clustering methods will divide the data into clusters, because that is what they are supposed to do. Therefore, before choosing a clustering approach, it is important to decide whether the dataset contains meaningful clusters. If the data does contain meaningful clusters, then the number of clusters is also an issue that needs to be looked at. This process is called assessing clustering tendency (feasibility of cluster analysis). To carry out a feasibility study of cluster analysis Hopkins statistic will be used to assess the clustering tendency of the dataset. Hopkins statistic assess the clustering tendency based on the probability that a given data follows a uniform distribution (tests for spatial randomness). If the value of the statistic is close to zero this implies that the data does not follow uniform distribution and thus we can reject the null hypothesis. Hopkins statistic is calculated using the following formula: Where xi is the distance between two neighboring points in a given, dataset and yi represents the distance between two neighboring points of a simulated dataset following uniform distribution. If the value of H is 0.5, this implies that and are close to one another and thus the given data follows a uniform distribution. The next step in the unsupervised learning method is to identify the optimal number of clusters. The Hopkins statistic for the digital camera dataset was found to be 0.00715041. Since Hopkins statistic was quite low, we can conclude that the dataset is highly clusterable. A visual assessment of the clustering tendency was also carried out and the result can be seen in Figure 8. Figure 8 Dissimilarity Matrix of the dataset DETERMINING OPTIMAL NUMBER OF CLUSTERS One simple solution to identify the optimal number of cluster is to perform hierarchical clustering and determine the number of clusters based on the dendogram generated. However, we will utilize the following methods to identify the optimal number of clusters: An optimization criterion such as within sum of squares or Average Silhouette width Comparing evidence against null hypothesis. (Gap Statistic) SUM OF SQUARES The basic idea behind partitioning methods like k-means clustering algorithms, is to define clusters such that the total within cluster sum of squares is minimized. Where Ck is the kth cluster and W(Ck) is the variation within the cluster. Our aim is to minimize the total within cluster sum of squares as it measures the compactness of the clusters. In this approach, we generally perform clustering method, by varying the number of clusters (k). For each k we compute the total within sum of squares. We then plot the total within sum of squares against the k-value, the location of bend or knee in the plot is considered as an appropriate value of the cluster. AVERAGE SILHOUETTE WIDTH Average silhouette is a measure of the quality of clustering, in that it determines the how well an object lies within its cluster. The metric can range from -1 to 1, where higher values are better. Average silhouette method computes the average silhouette of observations for different number of clusters. The optimal number of clusters is the one that maximizes the average silhouette over a range of possible values for different number of clusters [9]. Average silhouette functions similar to within sum of squares method. We carry out the clustering algorithm by varying the number of clusters, then we calculate average silhouette of observation for each cluster. We then plot the average silhouette against different number of clusters. The location with the highest value of average silhouette width is considered as the optimum number of cluster. GAP STATISTIC This method compares the total within sum of squares for different number of cluster with their expected values while assuming that the data follows a distribution with no obvious clustering. The reference dataset is generated using Monte Carlo simulations of the sampling process. For each variable (xi) in the dataset we compute its range [min(xi), max(xj)] and generate n values uniformly from the range min to max. The total within cluster variation for both the observed data and the reference data is computed for different number of clusters. The gap statistic for a given number of cluster is defined as follows: denotes the expectation under a sample of size n from the reference distribution. is defined via bootstrapping and computing the average . The gap statistic measures the deviation of the observed Wk value from its expected value under the null hypothesis. The estimate of the optimal number of clusters will be a value that maximizes Gapn(k). This implies that the clustering structure is far away from the uniform distribution of points. The standard deviation (sdk) of is also computed in order to define the standard error sk as follows: Finally, we choose the smallest value of the number of cluster such that the gap statistic is within one standard deviation of the gap at k+1 Gap(k)à ¢Ã¢â‚¬ °Ã‚ ¥Gap(k+1) sk+1 The above method and its explanation are borrowed from [10]. DATA PRE-PROCESSING The issue with K-means clustering is that it cannot handle categorical variables. As the K-means algorithm defines a cost function that computes Euclidean distance between two numeric values. However, it is not possible to define such distance between categorical values. Hence, the need to treat categorical data as numeric. While it is not improper to deal with variables in this manner, however categorical variables lose their meaning once they are treated as numeric. To be able to perform clustering efficiently, Gower distance will be used for clustering. The concept of Gower distance is that for each variable a distance metric that works well for that particular type of variable is used. It is scaled between 0 and 1 and then a linear combination of weights is calculated to create the final distance matrix. PARTITIONING METHODS K-MEANS K-means clustering is the simplest and the most commonly used partitioning method for splitting a dataset into a set of k clusters. In this method, we first choose K initial centroids. Each point is then assigned to the closest centroid, and each collection of points is assigned to a centroid in the cluster. The centroid of each cluster is updated based on the additional points assigned to the cluster. We repeat his until the centroids find a steady state. Figure 9 Plot Showing total sum of square and Average Silhouette width for different number of clusters We can see from Figure 9, that the optimal number of clusters suggested by the optimization criteria is 3 clusters using WSS method and 2 clusters using Average Silhouette width method. Considering the dependent variable is factor with two levels, having two clusters does make sense. The disadvantage of optimization criterion to identify the optimal clusters is that, it is sometimes ambiguous. A more sophisticated method is the gap statistic method. Figure 10 Gap Statistic for different number of clusters From Figure 10, we can see that the Gap statistic is high for 2 clusters. Hence, we carry out k-means clustering with 2 clusters on a majority basis. Figure 11 Visualizing K-means Clustering Method The data separates into two relatively distinct clusters, with the red category in the left region, while the region on the right contains the blue category. There is a limited overlap at the interface between the classes. To visualize K-means it is necessary to bring the number of dimensions down to two. The graph produced by fviz_cluster: Factoextra Ver: 1.0 [11] is not a selection of any two dimensions. The plot shows the projection of the entire data onto the first two principle components. These are the dimensions which show the most variation in the data. The 52.8% indicates that the first principle component accounts for 52.8% variation in the data, whereas the second principle component accounts for 23.9% variation in the data. Together both the dimensions account for 76.7% of the variation. The polygon in red and blue represent the cluster means. PARTITIONING AROUND MEDOIDS K means clustering is highly sensitive to outliers, this would affect the assignment of observations to their respective clusters. Partitioning around medoids also known as K-medoids clustering are much more robust compared to k-means. K-medoids is based on the search of medoids among the observation of the dataset. These medoids represent the structure of the data. Much like K-means, after finding the medoids for each of the K- clusters, each observation is assigned to the nearest medoid. The aim is to find K-medoids such that it minimizes the sum of dissimilarities of the observations within the cluster. Figure 12 Plot Showing total sum of square and Average Silhouette width for different number of clusters We can see from Figure 12, that the optimal number of clusters suggested by the optimization criteria is 3 clusters using WSS method and 2 clusters using Average Silhouette width method. Considering the dependent variable is factor with two levels, having two clusters does make sense. The disadvantage of optimization criterion to identify the optimal clusters is that, it is sometimes ambiguous. A more sophisticated method is the gap statistic method. Figure 13 Gap Statistic for different number of clusters From Figure 13, we can see that the Gap statistic is high for 2 clusters. Hence, we carry out partitioning around medoids clustering with 2 clusters on a majority basis. Figure 14 Plot visualizing PAM clustering method The data separates into two relatively distinct clusters, with the red category in the lower region, while the upper region contains the blue category. There is a limited overlap at the interface between the classes. fviz_cluster: Factoextra Ver: 1.0 [11] transforms the initial set of variables into a new set of variables through principal component analysis. This dimensionality reduction algorithm operates on the 72 variables and outputs the two new variables that represent the projection of the original dataset. CLUSTER VALIDATION The next step in cluster analysis is to find the goodness of fit and to avoid finding patterns in noise and to compare clustering algorithms, cluster validation is carried out. The following cluster validation measures to compare K-means and PAM clustering will be used: Connectivity: Indicates the extent to which the observations are placed in the same cluster as their nearest neighbors in the data space. It has a value ranging from 0 to à ¢Ã‹â€ Ã… ¾ and should be minimized Dunn: It is the ratio of shortest distance between two clusters to the largest intra-cluster distance. It has a value ranging from 0 to à ¢Ã‹â€ Ã… ¾ and should be maximized. Average Silhouette width The results of internal validation measures are presented in the table below. K-means for two cluster has performed better for each statistic. Figure 15 Plot Comparing Connectivity and Dunn Index for K-means and PAM for different number of clusters      Ã‚   Figure 16 Plot Comparing Average Silhouette width of K-means and PAM Clustering Algorithm Validation Measures Number of Clusters 2 3 4 5 6 kmeans Connectivity 139.9575 292.5563 406.5429 514.3913 605.5373 Dunn 0.0661 0.0246 0.0223 0.0244 0.0291 Silhouette 0.4369 0.3174 0.2814 0.2679 0.2447 pam Connectivity 156.1004 333.754 474.4298 520.3913 635.3687 Dunn 0.0275 0.0397 0.022 0.028 0.0246 Silhouette 0.4271 0.3035 0.2757 0.2661 0.2325 Table 1 Presenting the values of different validation measures for K-means and PAM Validation Measures Score Method Clusters Connectivity 139.9575 kmeans 2 Dunn 0.0661 kmeans 2 Silhouette 0.4369 kmeans 2 Table 2 Optimal Scores for the Validation Measures CONCLUSION In this research work, partitioning methods like K-means and Partitioning around medoids were developed. The performances of these two approaches have been observed on the basis of their Connectivity, Dunn index and Average Silhouette width. The results indicate that K-means clustering algorithm with K = 2 performs better than partitioning around medoids with two clusters. The findings of this paper will be very useful to predict obsolescence with higher accuracy. FUTURE WORK Advanced clustering algorithms such as Model based clustering and Density based clustering can be carried out to find the multivariate data structure as most of the variables are categorical. [1] Bjoern Bartels, Ulrich Ermel, Peter Sandborn and Michael G. Pecht (2012). Strategies to the Prediction, Mitigation and Management of Product Obsolescence. [2] Source Figure 1: https://www.statista.com/statistics/269927/sales-of-analog-and-digital-cameras-worldwide-since-2002/ [3] Source, Figure 1: https://www.statista.com/statistics/263437/global-smartphone-sales-to-end-users-since-2007/ [4] S. Still, and W. Bialek, How many Clusters? An Information Theoretic Perspective, Neural Computation, 2004. [5] Wickham, Hadley, rvest: Easily Harvest (Scrape) Web Pages. https://cran.r-project.org/web/packages/rvest/rvest.pdf, Ver. 0.3.2 [6] https://www.dpreview.com [7] Rubin, D.B., Inference and missing data. Biometrika, 1976. [8] Multivariate Imputation by Chained Equations Stef van Buuren, Karin Groothuis . [9] Learning the k in k-means Greg Hamerly, Charles Elkan [10] Robert Tibshirani, Guenther Walther and Trevor Hast

An Analysis of Political Elitism Essay -- Elitism Democracy Sociology

An Analysis of Political Elitism It is easy to believe that the middle-class working individual, whether he or she be white collar or blue collar, wields little political power except for during an election. It is also easy to think that we don’t have true democracy; political representation elected by the people, for the people, and controlled by these people. This is an ideology that is often worn out. Instead, these elected representatives are controlled by political à ©lites: high-ranking political "gladiators", the media, lobbyists, and, though it may not seem evident, big business. It is, in essence, commonly believed by most. Some reasons why political à ©lites at times dominate government and who these groups are will be examined in this essay. Also, there will be an analysis of those who were political à ©lites in Canada over the past centuries. Also, some new discoveries may be turned up that help us have a better understanding of this elitism. Finally, we will discuss if interest groups and mino rities have real political power, or perhaps they are just given token compensation. Hopefully, by the end of this essay, there will be a better understanding of who really has political power in Canada. Though this paper is an analysis of elitism, we must also dissect the concept of democracy. Needless to say, without democracy in a political system, elitism would not exist. Democracy was a concept developed by the Greeks and the Romans during the classical period. It comes from the Greek word "demos", which means "the people"; and "kratien", which means "to rule". In essence, democracy is a nation’s people rule themselves through elected representatives. Funk and Wagnalls Encyclopedia reminds us of an important point though. Though the words "democracy" and "republic" are used together universally, they are definitely not the same thing. For instance, Canada is defined as a constitutional monarchy. It is not a republic, yet, we use a democratic system. Another is China, who’s official title is "The People’s Republic of China"; yet, China is far from democratic. Furthermore, democracy is seen as ambiguous. Democracy is not only a concept on which our great natio n is based, but it is also a source for which government can use its authority, and it is also a process. This is where elitism is spawned. Elitism can be seen, from a certain point of view, as ... ...oronto: Key Porter Books Ltd., 1994. Dunn, Christopher. Canadian Political Debates. 1st ed Toronto: McClelland & Stewart Inc., 1995. Filemyr, Anne. "Conflict and Mainstream Reporting." Canadian Business and Canadian Affairs. 28.3 (August, 1996): 97-101. Francis, Diane. Controlling Interest: Who Owns Canada? 2nd ed. Toronto: Scorpio Publishing Ltd., 1986. Funk & Wagnalls. "Democracy" Funk & Wagnalls New Encyclopedia. 4th ed. New York: Funk & Wagnalls, Inc., 1983. Guy, James John. How we are Governed: The Basics of Canadian Politics and Government. 1st ed. Toronto: Harcourt Brace & Company Canada, Ltd, 1995. Jackson, Robert J.; Jackson, Doreen. Politics in Canada. 4th ed. Scarborough, Ontario: Prentice-Hall Canada Inc., 1998. Letter to the CBC ombudsman from the Prime Minister’s Office. Dated October 16, 1998. (www.tv.cbc.ca/cgi-bin/extlnk.cgi?/national/pgminfo/apec/pmo2.html) Penguin Books. The Penguin Dictionary of Sociology. 2nd ed. London: Penguin Books Ltd., 1994. Van Loon, Richard J.; Whittington, Michael S. The Canadian Political System: Environment, Structure and Process. 3rd rd. Toronto: McGraw-Hill Ryerson Publishing Ltd., 1981. An Analysis of Political Elitism Essay -- Elitism Democracy Sociology An Analysis of Political Elitism It is easy to believe that the middle-class working individual, whether he or she be white collar or blue collar, wields little political power except for during an election. It is also easy to think that we don’t have true democracy; political representation elected by the people, for the people, and controlled by these people. This is an ideology that is often worn out. Instead, these elected representatives are controlled by political à ©lites: high-ranking political "gladiators", the media, lobbyists, and, though it may not seem evident, big business. It is, in essence, commonly believed by most. Some reasons why political à ©lites at times dominate government and who these groups are will be examined in this essay. Also, there will be an analysis of those who were political à ©lites in Canada over the past centuries. Also, some new discoveries may be turned up that help us have a better understanding of this elitism. Finally, we will discuss if interest groups and mino rities have real political power, or perhaps they are just given token compensation. Hopefully, by the end of this essay, there will be a better understanding of who really has political power in Canada. Though this paper is an analysis of elitism, we must also dissect the concept of democracy. Needless to say, without democracy in a political system, elitism would not exist. Democracy was a concept developed by the Greeks and the Romans during the classical period. It comes from the Greek word "demos", which means "the people"; and "kratien", which means "to rule". In essence, democracy is a nation’s people rule themselves through elected representatives. Funk and Wagnalls Encyclopedia reminds us of an important point though. Though the words "democracy" and "republic" are used together universally, they are definitely not the same thing. For instance, Canada is defined as a constitutional monarchy. It is not a republic, yet, we use a democratic system. Another is China, who’s official title is "The People’s Republic of China"; yet, China is far from democratic. Furthermore, democracy is seen as ambiguous. Democracy is not only a concept on which our great natio n is based, but it is also a source for which government can use its authority, and it is also a process. This is where elitism is spawned. Elitism can be seen, from a certain point of view, as ... ...oronto: Key Porter Books Ltd., 1994. Dunn, Christopher. Canadian Political Debates. 1st ed Toronto: McClelland & Stewart Inc., 1995. Filemyr, Anne. "Conflict and Mainstream Reporting." Canadian Business and Canadian Affairs. 28.3 (August, 1996): 97-101. Francis, Diane. Controlling Interest: Who Owns Canada? 2nd ed. Toronto: Scorpio Publishing Ltd., 1986. Funk & Wagnalls. "Democracy" Funk & Wagnalls New Encyclopedia. 4th ed. New York: Funk & Wagnalls, Inc., 1983. Guy, James John. How we are Governed: The Basics of Canadian Politics and Government. 1st ed. Toronto: Harcourt Brace & Company Canada, Ltd, 1995. Jackson, Robert J.; Jackson, Doreen. Politics in Canada. 4th ed. Scarborough, Ontario: Prentice-Hall Canada Inc., 1998. Letter to the CBC ombudsman from the Prime Minister’s Office. Dated October 16, 1998. (www.tv.cbc.ca/cgi-bin/extlnk.cgi?/national/pgminfo/apec/pmo2.html) Penguin Books. The Penguin Dictionary of Sociology. 2nd ed. London: Penguin Books Ltd., 1994. Van Loon, Richard J.; Whittington, Michael S. The Canadian Political System: Environment, Structure and Process. 3rd rd. Toronto: McGraw-Hill Ryerson Publishing Ltd., 1981.

Perceptions of Administrative and Academic Support Services by Student :: essays research papers

Perceptions of Students in MSA Courses â€Å"Perceptions of Administrative and Academic Support Services by Students Taking Courses in the Master of Science in Administration Program† Abstract This paper focuses on the analysis of empirical data relating to the perceptions of students currently enrolled in courses offered in the Masters of Science in Administration (MSA) program at Saint Michael’s College (SMC) in Winooski, Vermont. A survey was designed and administered to 95 students in an effort to capture their perception of the quality of academic and administrative support services available to graduate students. Specific attention was given to the areas of enrollment services, financial services, library services and advising. The subjects generally rated these services somewhere between â€Å"average† and â€Å"excellent†. Comments written-in by subjects provide information that can be used to improve the students’ experience with various MSA Program services. Perceptions of Administrative and Academic Support Services by Students Taking Courses in the Master of Science in Administration Program at Saint Michaels College Customer perception surveys are a means of measuring how customers rate their experience with products or services. The result is a quantitative measure of their levels of satisfaction. By repeating the survey at regular intervals, it can be determined whether customers' perceptions are improving or deteriorating. Based on this information, changes can be made in services and marketing strategies. By later repeating the study, it can be determined how effective the changes have been in improving how a customer rates their experience. In an interview with Paul Olsen, Associate Director, Master of Science in Administration Program, we learned that the MSA program has not conducted a survey of students enrolled in the program to identify levels of satisfaction with services and programs offered. There has been one formal survey of alumni, completed in the spring of 1996 by SMC undergraduates in a Research Methods class (See Appendix D). This survey was conducted to gather information pertaining to overall satisfaction with the graduate program, whether the alumni’s goals and objectives had been met, and demographic data on program alumni. Our team believes that a survey of students currently taking MSA program course could provide information that would be tremendously helpful in assisting the college to assess the efficiency and quality of its services. Our operating premise is that an individual’s first experience with an organization has a direct impact on their long-term impressions and overall senses of connectedness. Therefore, Perceptions of Administrative and Academic Support Services by Student :: essays research papers Perceptions of Students in MSA Courses â€Å"Perceptions of Administrative and Academic Support Services by Students Taking Courses in the Master of Science in Administration Program† Abstract This paper focuses on the analysis of empirical data relating to the perceptions of students currently enrolled in courses offered in the Masters of Science in Administration (MSA) program at Saint Michael’s College (SMC) in Winooski, Vermont. A survey was designed and administered to 95 students in an effort to capture their perception of the quality of academic and administrative support services available to graduate students. Specific attention was given to the areas of enrollment services, financial services, library services and advising. The subjects generally rated these services somewhere between â€Å"average† and â€Å"excellent†. Comments written-in by subjects provide information that can be used to improve the students’ experience with various MSA Program services. Perceptions of Administrative and Academic Support Services by Students Taking Courses in the Master of Science in Administration Program at Saint Michaels College Customer perception surveys are a means of measuring how customers rate their experience with products or services. The result is a quantitative measure of their levels of satisfaction. By repeating the survey at regular intervals, it can be determined whether customers' perceptions are improving or deteriorating. Based on this information, changes can be made in services and marketing strategies. By later repeating the study, it can be determined how effective the changes have been in improving how a customer rates their experience. In an interview with Paul Olsen, Associate Director, Master of Science in Administration Program, we learned that the MSA program has not conducted a survey of students enrolled in the program to identify levels of satisfaction with services and programs offered. There has been one formal survey of alumni, completed in the spring of 1996 by SMC undergraduates in a Research Methods class (See Appendix D). This survey was conducted to gather information pertaining to overall satisfaction with the graduate program, whether the alumni’s goals and objectives had been met, and demographic data on program alumni. Our team believes that a survey of students currently taking MSA program course could provide information that would be tremendously helpful in assisting the college to assess the efficiency and quality of its services. Our operating premise is that an individual’s first experience with an organization has a direct impact on their long-term impressions and overall senses of connectedness. Therefore,

War of 1812 Essay -- essays research papers

The War of 1812   Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  The war of 1812, supposedly fought over neutral trading rights, was a very peculiar conflict indeed. Britain's trade restrictions, one of the main causes, were removed two days before the war started; the New Englanders, for whom the war was supposedly fought, opposed it; the most decisive battle, at New Orleans, was fought after the war ended.   Ã‚  Ã‚  Ã‚  Ã‚  During the Napoleonic wars, Britain and France had disrupted US shipping, confiscated American goods, taking US seamen into the British navy, and both sides had blockaded each other's ports. This caused great annoyance to American traders, and Britain's abduction of American sailors especially caused great uproar and indignation at home. Many called for war, although it is interesting to note that it was southerners and westerners, the so-called war hawks led by Clay and Calhoun, who supported war who were least affected by Britain's actions. Some historians attribute this to their desire to take British Canada and Spanish Florida in the process of war. The Republican administration, traditionally supportive of France, finally declared war on Britain in 1812, ironically two days after Britain had lifted their trade embargo.   Ã‚  Ã‚  Ã‚  Ã‚  Two and a half years of fighting commenced, and when the peace treaty was eventually signed in Ghent, there was no mention whatsoever of neutral rights. The treaty gave neither si...

John Locke Paper

Throughout the 17th century, John Locke presented society with his teachings and theories that clarified the order of natural law and fulfilled humanity’s divine purpose for living. It all began in 1647, as a young boy when he attended the prestigious Westminster School in London under the sponsorship of Alexander Popham. During his years at the Westminster School, he found the work of modern philosophers more interesting than the material being taught at the university.Much of Locke's influence and later work was characterized by opposition to authoritarianism, which focused on both the level of the individual person and on the level of institutions such as government and church. Locke wanted each of us to use reason to search after truth rather than simply accept the opinion of authorities or be subject to superstition. He wanted us to proportion go along with the proposition to the evidence for them. Locke came to the conclusion that there must be a balance and mutual under standing between individuality and social institutions where society will not feel suppressed under man made law and restrictions.John Locke believed that all knowledge comes from experience. Experience is composed of two parts: external and internal. External experiences are ideas of supposed external objects. These objects enter our minds through sensation. Examples of sensations would be hot, cold, red, yellow, hard, soft, sweet and bitter. Internal experiences are reflections that make us understand the operation on the objects of sensation. Examples of reflections are thinking, willing, believing, doubting, affirming, denying, and comparing.Once again Locke goes back to his foundation of principles by reaffirming that in order to achieve success and sensation there must be a working relationship between individual goals and the law of society. Sensation and reflection are called the two fountains of knowledge. All of our ideas we can naturally have or have so already come from these two experiences. Sensible qualities convey into the mind, and they produce most of the perceptions and most of the great sources of ideas we have.Sensation and reflection differ from each other because sensation is what happens outside the body, and reflection has to do what happens inside the body with our mind. Also reflection has to do with the ideas it affords being such only as the mind gets by reflecting on its own operations within itself, the mind takes over its own operations and the manner of them. Besides having sensible qualities one also contains primary and secondary qualities. Locke explains that these qualities are two kinds of properties that an object could have.Primary qualities contain solidity, figure, extension, motion and number. They are properties that are objective and independent on senses. On the other hand, secondary qualities consist of color, smell, taste, sound and touch. They are properties that are subjectively perceived. In Locke’s, An Essay Concerning Human Understanding, he states, â€Å"sensible qualities; which, whatever reality we by mistake attribute to them, are in truth nothing in the objects themselves, but powers to produce various sensations in us†¦Ã¢â‚¬  (John Locke, 77).In other words, secondary qualities are dependent on the primary qualities. According to Locke, ideas are anything that is â€Å"the immediate object of perception, thought, or understanding† (William Lawhead, 91). Locke states that sensation and reflection are classified as simple and complex ideas. Simple ideas are red, yellow, hard, soft, etc and for example, you touch an ice cube, your mind is telling you its cold and it’s hard, you learn that from experience. Locke believed that the mind cannot know an inexperienced idea or create a new simple idea.Although the mind cannot create simple ideas, it can process them into complex ideas. Complex ideas are made up of several simple ideas, such as beauty, gratitude, a man, an army, the universe. Complex ideas are also broken down into three parts: ideas of substance which is a constant collection of simple ideas, ideas of mode which is a combination of several ideas, which form a mode, like a triangle, last but not least ideas of relationship, which is a comparison of one idea to another.From experience it goes to sensation and reflection, and those are based on simple ideas and that’s all contained in the passive mind, after simple ideas it goes to complex ideas and that’s located in the active mind. Overall in Locke’s theory he uses epistemological dualism, which is the mind that consists of knowing and its ideas. He also states the object in the external world is known by ideas, and our ideas represent those objects. After researching about Locke’s theory of knowledge I would have to agree with what he has stated.Locke states that you go through an internal and external experience and I feel that today’s y outh do go through the motions of the internal and external experiences. As a result the youth are able to gain the knowledge from those experiences by allowing the mind to willingly accept these new ideas. For example, when I was younger I put my hand near a hot stove and from the heat irritating and pressuring my hand my mind told me it was a negative stimuli and it was essential to remove my hand from the stove and to keep that memory as a basic instinct.Society goes through experiences throughout life of internal and external and eventually gains knowledge through these experiences. John Locke also stated that the mind does all the knowing and its ideas are known. I agree with what he is saying because your mind is always working, it’s always active, we receive ideas internally through our mind and we receive ideas from the outside that goes into our mind. The balance is necessary between internal and external factors to keep society and individuals stable and yet progres sive to adapt to new changes that rise up.