# Pairwise Overlap and Misclassification in Cluster. Analysis. Birzhan Akynkozhayev. Nazarbayev University 2015

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1 Pairwise Overlap and Misclassification in Cluster Analysis by Birzhan Aynozhayev Supervisor: Igor Melnyov Second reader: Ayman Alzaatreh Nazarbayev University 2015

2 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 2 Contents 1. Introduction The case of two clusters Mixture model Simulations Modeling misclassification probabilities Logistic regression model Linear regression model Quadratic regression model The case of multiple clusters Introduction to emem Method Initialization of algorithm Simulations Modeling AR index Logistic regression model Linear regression model Second order regression model Discussion References Appendix... 26

3 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 3 1. Introduction Separation of data into distinct groups is one of the most important tools of learning and means of obtaining valuable information from data. Cluster analysis studies the ways of distributing objects into groups with similar characteristics. Real-world examples of such applications are age separation of a population, loyalty grouping of customers, classification of living organisms into ingdoms, etc. In particular, cluster analysis is an important objective of data mining, which focuses on studying ways of extracting ey information from data and converting it into some more understandable form. There is no single best algorithm for producing data partitions in cluster analysis, but many that perform well in various circumstances Jain, 2008). Many popular clustering algorithms are based on an iterative partitioning method, where single items are moved step-by-step from one cluster to another based on optimization of some parameter. One of such algorithms, which will be mentioned in this paper is K-means algorithm, where data points are partitioned based on optimization of sum of squared distances within clusters MacQueen, 1967). Another large class of algorithms are based on finite mixture model clustering methods. For example, stochastic ememclustering method, which will also be covered in this article, is based on maximum lielihood estimation of statistical model parameters Melnyov & Maitra).

4 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 4 Figure 1. a) True partition b) Partition obtained by K-means algorithm Misclassification of data is not a rare situation in cluster analysis. For instance, we can observe that several points have been misclassified on the previous figure Figure 1) of true partition a) versus the solution found by the K-means algorithm b). Various factors lead to misclassification in clustering algorithms. The main goal of this paper is to analyze the effect of pairwise overlap, number of dimensions of data, and number of clusters on misclassification. The simplest case where misclassification can occur is when there are two clusters. The overlap is exact in this case, thus, we proceeded to use one of the simplest algorithms K-means. At the higher number of clusters, when overlap is estimated, we considered more complex emem algorithm. 2. The case of K = 2 clusters Firstly, we began our investigation with the most simple scenario in which misclassification can occur, a case of two clusters. Most methods provide similar solutions in this case. Thus, we decided to use one of the simplest and fastest algorithms, which is K-means. Although K-means algorithm was published in 1950s and is 60 years old Lloyd, 1957), it is still one of the most widely used and popular algorithms today. This algorithm aims at minimizing the following objective function: n J = x i j) c j 2 j=1 i=1 Here, x i j) c j is the distance between cluster center c j and data point x i j). The K-means algorithm distributes N points in p dimensional space into K clusters, based on the minimization of sum of squared distances within clusters MacQueen, 1967)

5 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 5 The algorithms consists of the following steps: 1) Pic K random points from the data set, these points will represent initial cluster centers. 2) Assign each point to the cluster with the closest center. 3) Calculate clusters geometrical centers and assign them to be new centers. 4) Repeat Steps 2 and 3 until the centers are stabilized. K-means algorithm is sensitive to the choice of initial centers. In some cases algorithm may not converge or one or more clusters can get dissolved. In that case, the algorithm is repeated with a different set of initial centers. The solution that produces the lowest value of objective function J is recorded as the best. 2.1 Mixture model Other partition optimization algorithms rely on parametric methods, such as finite mixture model techniques. A mixture model is a statistical model, which specifies presence of subclasses in a data set, without identification to which subclass individual points belong McLaughlan & Peel, 2000). For independent identically distributed p-dimensional observations X1, X2,,Xn, the probability density function for mixture model with K components is K f x; ) f 1 x) where f is the th component and π is the probability that observation belongs to th K component π 0, 1). Commonly, f is a normal Gaussian) density φ x μ, ), 1 where μ is the mean and is the covariance matrix. φ x μ, ) = exp { 1 2 x i μ ) 1 x i μ )} 2π) p 2 1 2

6 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 6 Pairwise overlap is the measure of how much clusters penetrate each other. Pairwise overlap is the sum of misclassification probabilities i j and j i Melnyov & Maitra, 2009) Pr[ x;, ) x;, ) x ~ N, )] j i i i i j j j p i i To analyze the degree of misclassification, we tried to fit different regression models to observe the behavior of misclassification probability relevant to overlap and number of dimensions of cluster data. Initially, we expected that misclassification would be higher for higher number of dimensions of data, since points can be close to each other in one dimension and be greatly separated in another. 2.2 Simulations To generate data we used MixSim R pacage, which provides ways to generate multidimensional and multi-component Gaussian mixtures and specifies mean and maximum overlap between clusters in mixtures Melnyov, Chen & Maitra, 2013). Datasets of sample size 1000 were generated using finite mixture model with Gaussian components for pre-specified level of maximum overlap between clusters. Covariance matrix structure was set to be spherical and the value of smallest mixing proportion was set to imply equal proportions simulations of such datasets were used to obtain the median values of misclassification probabilities for each variation of overlap ω) and number of dimensions p). The following results Table 1) were obtained for median values of misclassification proportions: w/p p=2 p=3 p=4 p=5 p=7 p=10 w=0.01 0, w= w= w= w= w= w=

7 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 7 Table 1. Median misclassification probabilities for K=2. Misclassification was measured by two parameters, adjusted Rand AR) index and percentage of misclassification. AR index describes the agreement between two data partitions and attains value 1 when they agree perfectly. This index has the expected value equal to 0 when the data points are allocated to clusters completely at random Hubert & Arabie, 1985). Since, labeling of the points as correctly or incorrectly classified is easy in the case of two clusters, we decided to use misclassification probability in that case. However, when the number of clusters is larger, the assignment of labels to the points is not trivial and calls for a more advanced measure, such as the adjusted Rand index. 3. Modeling misclassification probabilities 3.1 Logistic regression model Initially, we tried to fit a logistic function to define the behavior of misclassification probability subject to overlap ω) and number of dimensions p). One of the reasons to choose the logistic model was the value of misclassification probability that ranges between 0 and 1, i.e. its value approaches the horizontal asymptotes at 0 and 1 value 0 when there is no misclassification, value 1 when every point is misclassified). Assuming the failure or success of correctly identifying the case that a point belongs to a certain cluster as response variable Yi, one may consider Y as a Bernoulli random variable with parameter E{Y} = τ. Yi, can tae value 0 with probability 1 - τ, which is a case of misclassifying a point, and value 1 with probability τ, i.e. correctly classifying that point. The expected value of Yi is as follows: eβ 0+β 1 ω+β 2 p E{Y i } = 1 + e β 0+β 1 ω+β 2 p The results show that misclassification probability of K-means algorithm depends on the overlap between clusters and that the number of dimensions p is not a significant predictor in the case of two clusters. Table 2).

8 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 8 Call: glmformula = misclass ~ w + p, family = "binomial") Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr> z ) Intercept) * w p Signif. codes: 0 *** ** 0.01 * Dispersion parameter for binomial family taen to be 1) Null deviance: on 41 degrees of freedom Residual deviance: on 39 degrees of freedom AIC: Number of Fisher Scoring iterations: 6 Table 2. Summary of regression of misclassification percentage on degree of overlap ω) and number of dimensions p). Therefore, we excluded the number of dimensions from the regression model and obtained the following result Table 3) for the logistic model. Call: glmformula = misclass ~ w, family = "binomial") Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr> z ) Intercept) ** w Signif. codes: 0 *** ** 0.01 * Dispersion parameter for binomial family taen to be 1) Null deviance: on 41 degrees of freedom Residual deviance: on 40 degrees of freedom AIC: Number of Fisher Scoring iterations: 6 Table 3. Summary of regression of misclassification percentage on degree of overlap ω) Thus, the analytic expression of the regression function is as follows: e ω τ = 1 + e ω

9 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 9 Figure 2. Observed values of misclassification probabilities and the graph of the fitted logistic model. However, it can be observed from the graph in Figure 2 that the fit is quite poor; in addition, the chi-square goodness-of-fit test suggests that it is very unliely p-value < 0.001) that observed data comes from our logistic regression model. Thus, we had to search for a better fitting model. 3.2 Linear regression model After denying logistic model, we considered the use of a linear model. Similarly, we tried to fit both predictor variables ω and p. The results are as follows: Call: lmformula = misclass ~ w + p) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr> t ) Intercept) ** w < 2e-16 *** p * ---

10 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 10 Signif. codes: 0 *** ** 0.01 * Residual standard error: on 39 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: 5630 on 2 and 39 DF, p-value: < 2.2e-16 Table 4. Summary of linear regression of misclassification percentage on pairwise overlap and number of dimensions. Again, number of dimensions parameter is not significant, thus we excluded it from further consideration: τ = ω Call: lmformula = misclass ~ w) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr> t ) Intercept) e-08 *** w < 2e-16 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 40 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: 9734 on 1 and 40 DF, p-value: < 2.2e-16 Table 5. Summary of linear regression of misclassification percentage on pairwise overlap.

11 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 11 Figure 3. Observed values of misclassification probabilities and the graph of the fitted linear model. We evaluated the assumptions of the model and in particular tested the residuals for normality. Shapiro-Wil test of normality for residuals yielded p-value = Thus, the test of normality does not contradict the assumption that errors follow a normal distribution. Although the results for linear regression seemed to be reasonable enough, the following plot of residuals versus fitted values Figure 4) is not evenly distributed and shows quadratic tendency. Therefore, we looed for a better model, and proceeded to the analysis of a quadratic model in ω. Figure 4. Residuals of linear model in ω versus the fitted values 3.3 Quadratic regression model To obtain a better regression model, we further used a quadratic model to estimate the behavior of misclassification probability. The results of regression with both predictor variables ω and p: Call: lmformula = misclass ~ w + Iw^2) + p + Ip^2)) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr> t ) Intercept) 9.742e e w 6.232e e < 2e-16 *** Iw^2) e e *** p 6.759e e

12 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 12 Ip^2) e e Signif. codes: 0 *** ** 0.01 * Residual standard error: on 37 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: 3913 on 4 and 37 DF, p-value: < 2.2e-16 Table 6. Summary of quadratic regression of misclassification percentage on pairwise overlap and number of dimensions. Again, results show the insignificance of number of dimensions parameter. Therefore, it was removed from the model: τ = ω ω 2 Call: lmformula = misclass ~ w + Iw^2)) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr> t ) Intercept) ** w < 2e-16 *** Iw^2) *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 39 degrees of freedom Multiple R-squared: 0.997, Adjusted R-squared: F-statistic: 6478 on 2 and 39 DF, p-value: < 2.2e-16 Table 7. Summary of quadratic regression of misclassification percentage on pairwise overlap. Shapiro-Wil test of normality for residuals resulted in p-value = The test does not contradict that residuals have a Gaussian distribution. Unlie the residuals in a linear model, the following plot Figure 5) represents that residuals in quadratic model are more evenly distributed.

13 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 13 Figure 5. Residuals of quadratic model in ω versus the fitted values We were satisfied with the results of quadratic regression, thus we refrained from considering other models and indicated quadratic model as our most successful fit. The following plot Figure 6) represents observed values and fitted graph of quadratic model: Figure 6. Observed values of misclassification probabilities and the graph of the fitted quadratic model.

14 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS The case of multiple clusters 4.1 Introduction to emem Method After, studying the misclassification for number of clusters K=2, we wanted to proceed further and study how misclassification occurs with a larger number of clusters. Though previous results of misclassification percentages, which were obtained for number of clusters K=2, seemed to be decent, with the increase in K, K-means algorithm rapidly deteriorate. For example, the simulation of 1000 runs of K-means algorithm with K=2 and maximum overlap ω=0.1 gives median adjusted Rand AR) index of , however, the simulation with the same parameters except for K=5, shows median AR index of Thus, we sought for a better algorithm to obtain good solution for multiple clusters. Another popular clustering algorithm that we made an analysis on was Expectation-Maximization EM) algorithm. The EM algorithm is a general statistical method of maximum lielihood estimation and in particular it can be used to perform clustering Ordonez & Omiecinsi, 2002). The EM algorithm step by step improves a starting clustering model to better fit the data set and stops at a solution which is locally ideal or a saddle point Bradley, Fayyad & Reina, 1998). In analysis of EM algorithm, we continued to use Gaussian mixture as our choice for mixture model. The mixture density of which is the following: K f x; ) x;, ) 1 where μ is mean and is covariance matrix for -th component. The normal Gaussian) density φ x μ, ) is: φ x μ, ) = exp { 1 2 x i μ ) 1 x i μ )} 2π) p 2 1 2

15 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 15 According to V. Melnyov and R. Maitra 2009) the EM algorithm is carried out by assuming that there are missing data points, which together with the observed data compose complete data. The appropriate lielihood function is commonly easier to operate. Two steps, the expectation E) and the maximization M), compose the algorithm. The M-step of s-th iteration aims to maximize the conditional loglielihood function, called Q- function, with respect to parameter vector ),...,,, ; : 2 1 1) n s x x x Q The corresponding Q-function is given by: n i K i n i K i i i n s pn x x x x x Q ). log2 2 log )} ) {log 2 1 ),...,,, ; Given the current parameter θ s 1 estimates, E-step focuses on calculation of the following conditional probabilities: K s i s s i s s i th i s i x f x f X cluster X 1 1) 1) 1) 1) 1) ) ) ; ) ; } ; Prob{ Considering covariance matrix as a general unstructured dispersion matrix, the M-step gives the following solutions: n i s i i n i s i s n i s i s x n 1 ) 1 ) ) 1 ) ), 1

16 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 16 s) n i1 x ) x s) i i n i1 s) s) i i s) ). When the respective increase in lielihood function is not considerable, the algorithm is stopped. 4.2 Initialization of the algorithm The lielihood function commonly can have multiple local maxima, thus the algorithm is very sensitive to the choice of starting points and good initialization is critical. There exist numerous initialization algorithms Melnyov & Maitra, 2009). Since we chose the stochastic emem method proposed by V. Melnyov and R. Maitra for clustering, the initialization was carried out as a part of the method. The emem algorithm consists of two EM stages. First one, called short EM, runs EM algorithm with set of initial points chosen randomly. The solution, which produces the best log lielihood, is used afterwards as an initializer for the second long EM stage. The long EM runs until convergence criterion is met and final solution is obtained. 4.3 Simulations The generation of datasets was similar to the simulations done for K-means algorithm. We used MixSim pacage, mentioned before and EMCluster R pacage, which provides ways for execution of EM algorithm. Datasets were again of size 1000 and generated using Gaussian finite mixture model with pre-specified levels of maximum overlap between clusters. The value of the smallest mixing proportion was set to imply equal proportions and covariance matrix structure was set to be non-spherical. Due to the complexity of algorithm, unlie for K-means we did 100 simulations of such datasets for obtaining values of AR index for each value of overlap ω) and number of dimensions p). Unlie the case of K=2 number of clusters, where we used misclassification percentage as a measure of misclassification, here we used AR index. The

17 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 17 reason for the change was the difficulty in calculating misclassification percentages; thus, the following results Table 6) were obtained for the median AR index: w/p p=2 p=4 p=5 p=7 p=10 w=0, w=0, w=0, w=0, w=0, w=0, w=0, w=0, w=0, Table 6.Medians of AR index values for K=5 number of clusters 5. Modeling AR Index 5.1 Logistic regression model The value of AR index ranges between 0 and 1, thus first of all we tried to fit a logistic model. The summary of logistic regression Table 8) shows that number of dimensions is not significant parameter. e ω p AR = 1 + e ω p Call: glmformula = ARIndex ~ w + p, family = "binomial") Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr> z ) Intercept) * w ** p Signif. codes: 0 *** ** 0.01 * Dispersion parameter for binomial family taen to be 1) Null deviance: on 44 degrees of freedom Residual deviance: on 42 degrees of freedom AIC: Number of Fisher Scoring iterations: 4

18 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 18 Table 8. Summary of regression of AR index on degree of overlap ω) and number of dimensions p). Therefore, we excluded the number of dimensions from the regression model. However, it should be noted that p-value is larger in this case, thus the number of dimensions is less significant than in the case of K=2 clusters. The summary of regression Table 9) is the following: Call: glmformula = ARIndex ~ w, family = "binomial") Deviance Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error z value Pr> z ) Intercept) ** w ** --- Signif. codes: 0 *** ** 0.01 * Dispersion parameter for binomial family taen to be 1) Null deviance: on 44 degrees of freedom Residual deviance: on 43 degrees of freedom AIC: Number of Fisher Scoring iterations: 4 Table 9. Summary of logistic regression of AR index on degree of overlap ω) The result of regression: e ω AR = 1 + e ω The chi-square goodness of fit shows p-value < 0.001, suggesting that logistic model is a poor fit for observed data. Hence, we proceeded to consideration of a linear model.

19 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS Linear regression model As the logistic model was unsatisfactory, a linear model in ω and p was tried. The result for linear model was the following: Call: lmformula = AR ~ w + p) AR = ω *p Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr> t ) Intercept) < 2e-16 *** w < 2e-16 *** p e-06 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 42 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: on 2 and 42 DF, p-value: < 2.2e-16 Table 10. Summary of linear regression of median AR index on pairwise overlap and number of dimensions Due to the problems with curvature that we experienced before, the residuals were examined by means of the plot of residuals versus pairwise overlap Figure 7). Once again it was clear that a quadratic term in ω needs to be added. We fitted a model with both quadratic terms in ω and p) as well as the interaction term. Figure 7. Residuals of linear model in ω versus the fitted values.

20 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS Second order regression model Due to potential problems with high correlation among predictors and high order terms, the centering of predictor variables was performed. Those variables are ω c and p c, where ω c i = ω i ω and p c i = p i p. The results of regression Table 11): Call: lmformula = AR ~ wc + Iwc^2) + pc + Ipc^2) + wc:pc) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr> t ) Intercept) < 2e-16 *** wc < 2e-16 *** Iwc^2) < 2e-16 *** pc < 2e-16 *** Ipc^2) wc:pc e-05 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 39 degrees of freedom Multiple R-squared: , Adjusted R-squared: F-statistic: 783 on 5 and 39 DF, p-value: < 2.2e-16 Table 11. Summary of second order regression of AR index on pairwise overlap and number of dimensions. All terms except the quadratic term for the number of dimensions were significant. Thus, only that term had to be dropped and the model was refit: Call: lmformula = AR ~ wc + Iwc^2) + pc + wc:pc) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr> t ) Intercept) < 2e-16 *** wc < 2e-16 *** Iwc^2) < 2e-16 *** pc < 2e-16 *** wc:pc e-05 *** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 40 degrees of freedom Multiple R-squared: 0.99, Adjusted R-squared: F-statistic: on 4 and 40 DF, p-value: < 2.2e-16 Table 12. The summary of the final second order regression of AR index on pairwise overlap and number of dimensions.

21 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 21 The plots of residuals versus both predictor variables as well as fitted values Figure 8 a, b, c) were examined and did not reveal further problems. a) b)

22 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 22 c) Figure 8. Residuals of second order model plotted against a) fitted values, b) ω c, c) p c Figure 9. The histogram of residuals of the final model.

23 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 23 Shapiro-Wil test of normality for residuals resulted in p-value = The assumption of a normal distribution of errors is upheld. Rewriting the model in terms of non-centered variables we obtain the following equation: AR = *ω * ω * p *ω*p Outside of the scope of our model, an extrapolation would lead to inaccurate results. Thus, the model should be used for values of p between 2 and 10, ω between and 0.15 and number of clusters K=5. We can observe that both predictors negatively affect the values of AR index and in addition the interaction term between ω and p has a negative coefficient meaning that the adverse effect of the increase in number of dimensions and pairwise overlap on AR index is reinforced when both of them occur at the same time. We stopped further model selection and chose the second order model to be our best fit to the observed data. 6. Discussion This study analyzed the impact of several parameters on misclassification of data in cluster analysis. In particular, pairwise overlap and number of dimensions were studied as such predictors. Different algorithms were considered for different number of clusters: K-means algorithm in the case of two clusters and emem method in the case when the number of clusters is greater than two. The results that were obtained showed that pairwise overlap was a significant factor in both cases. However, the number of dimensions was not a significant factor in the case of two clusters, but it was a considerable factor in the case of multiple clusters. In general, a higher number of dimensions or higher pairwise overlap mean that misclassifications will occur more frequently. Logistic, linear, and second order regression models were tried as possible

24 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 24 approximations. Due to poor fitting characteristics, both the logistic and linear model were rejected. In the case of multiple clusters, regression shows that both overlap and number of dimensions are significant as well as interaction between them. In both cases, a second order regression model provided the best results.

25 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 25 References Bradley, P.S., Fayyad U., Reina C. 1998). Scaling EM Expectation-Maximization) Clustering to Large Databases. Report No. MSR-TR-98-35). Redmond: Microsoft Research. Hubert, L. & Arabie, P. 1985). Comparing partitions. Journal of Classification doi: /BF Jain, A.K. 2008). Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters, Vol. 31, No. 8, pp , Lloyd, S.P. 1982). Least Squares Quantization in PCM. IEEE Transactions on Information Theory. Vol. it-28, no.2, MacQueen, J. B. 1967). Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Bereley Symposium on Mathematical Statistics and Probability. University of California Press. pp McLachlan, G. & Peel, D. 2000). Finite mixture models. New Yor: Wiley. Melnyov, V. & Maitra, R. 2009). Finite mixture models and model-based clustering. Statistics Surveys. Vol ) ISSN: DOI: /09-SS053 Melnyov, V., Maitra, R., Chen, W. 2013). Simulating Data to Study Performance of Clustering Algorithms. CRAN Repository. Ordonez, C. & Omiecinsi, E. 2002). FREM: fast and robust EM clustering for large data sets. Proceedings of the eleventh international conference on Information and nowledge management. CIKM '02). ACM, New Yor, NY, USA, DOI= /

26 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 26 Appendix Code for simulation of K-means method: v<-vector) m<-vector) for in 1:10) { for o in 1:1000) { A<-MixSimMaxOmega=0.001*, p=2, K=5, PiLow=1,sph=TRUE) B<-simdataset1000, Pi = A\$Pi, Mu=A\$Mu, S=A\$S) trueid<-b\$id x<-b\$x Q<-meansx,5,nstart = 10000,iter.max=200,algorithm="Lloyd") estid<-q\$cluster t<-tabletrueid,estid) nom<-0 for i in 1:5) { column<-t[,i] maxpos<-which.maxcolumn) nom<-nom+sumcolumn)-t[maxpos,i] forj in 1:5) { ifj!=maxpos) { t[j,i]<-0 } } } for i in 1:5) { row<-t[i,] maxpos<-which.maxrow) nom<-nom+sumrow)-t[i,maxpos] } v[o]<-nom }

27 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 27 m[]<-medianv) } Code for simulation of emem method: libraryemcluster) librarymixsim) librarye1071) n<-1000 K<-10 p<-2 v<-c) for w in 1:100) { A<-MixSimBarOmega=0.01, p=2, K=10, PiLow=1,sph=FALSE) B<-simdatasetn, Pi = A\$Pi, Mu=A\$Mu, S=A\$S) trueid<-b\$id x<-b\$x dimx)<-dimb\$x) #### Clustering after initialization with the true cluster centers ##### gamma <- matrixrep0, n*k), ncol = K) for i in 1:n){ } gamma[i,trueid[i]] <- 1 init <- m.stepx, Gamma = gamma) Qtarget <- emclusterx, pi = init\$pi, Mu = init\$mu, LTSigma=init\$LTSigma, assign.class = TRUE) targetid<-qtarget\$class targetar<-classagreementtabletrueid,targetid))\$crand

28 PAIRWISE OVERLAP AND MISCLASSIFICATION IN CLUSTER ANALYSIS 28 #### Clustering after random initialization ##### bestbic <- Inf.EMC\$short.iter <- n / 5.EMC\$short.eps < EMruns <- 100 for i in 1:100) { Q <- em.emx, nclass = K) estid<-q\$class m <- K K * p + K * p * p + 1) / 2 BIC <- -2 * Q\$llhdval + m * logn) AR <- classagreementtabletrueid, estid))\$crand if BIC < bestbic){ bestbic <- BIC bestar <- AR } } cat"run =", w, " : Target AR =", targetar, " Found AR =", AR, "\n") plotx, col=estid) v<-cv, AR) } # end of loop in w

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