Variable Selection and Outlier Detection for Automated K-means Clustering
Sung‐Soo Kim · Communications for Statistical Applications and Methods · 2015
An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis.Here we provide an automated K-means clustering process combined with variable selection and outlier identification.The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner.To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001).To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach.Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers.The implemented R program can be obtained at http://www.knou.ac.kr/∼sskim/SVOKmeans.r.