Email this article 
Hide
Show all
| 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:: 2078. Submitted: 2013-04-04. Published: 2016-08-28. |
|||||||
| Paper: |
RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm
Download PDF
(Downloads: 3452)
|
||||||
| Supplements: |
| ||||||
| DOI: |
10.18637/jss.v072.i05
|
This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license. |