A Simple Approach to Sparse Clustering

Abstract

Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced. The new method is shown to be competitive with these two methods and others.

References in corpus (3)

• Natural Scales in Geographical Patterns
• Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables
• Phase Transitions for High Dimensional Clustering and Related Problems

Cited by in corpus (2)

• A Strongly Consistent Sparse $k$-means Clustering with Direct $l_1$ Penalization on Variable Weights
• Feature Screening in Large Scale Cluster Analysis