RSKC: An R package for a robust and sparse k-means clustering algorithm

Witten and tibshirani (2010) proposed an algorithim to simultaneously find clusters and select clustering variables,called sparse k-means (sk-means). sk-means is particularly useful when the dataset has a large fraction of noise variables (that is,variables without useful information to separate the clusters). sk-means works very well on clean and complete data but cannot handle outliers nor missing data. to remedy these problems we introduce a new robust and sparse k-means clustering algorithm implemented in the r package rskc. we demonstrate the use of our package on four datasets. we also conduct a monte carlo study to compare the performances of rsk-means and sk-means regarding the selection of important variables and identification of clusters. our simulation study shows that rsk-means performs well on clean data and better than sk-means and other competitors on outlier-contaminated data. © 2016,american statistical association. all rights reserved.