Authors: | Yumi Kondo, Matias Salibian-Barrera, Ruben Zamar | ||||||
Title: | RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm | ||||||
Abstract: | 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. | ||||||
Page views:: 3634. Submitted: 2013-04-04. Published: 2016-08-28. |
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Paper: |
RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm
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DOI: |
10.18637/jss.v072.i05
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