Simple and Scalable Sparse k-means Clustering via Feature Ranking

Abstract

Clustering, a fundamental activity in unsupervised learning, is notoriously difficult when the feature space is high-dimensional. Fortunately, in many realistic scenarios, only a handful of features are relevant in distinguishing clusters. This has motivated the development of sparse clustering techniques that typically rely on k-means within outer algorithms of high computational complexity. Current techniques also require careful tuning of shrinkage parameters, further limiting their scalability. In this paper, we propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms. We show that our algorithm enjoys consistency and convergence guarantees. Our core method readily generalizes to several task-specific algorithms such as clustering on subsets of attributes and in partially observed data settings. We showcase these contributions thoroughly via simulated experiments and real data benchmarks, including a case study on protein expression in trisomic mice.

References in corpus (6)

• Natural Scales in Geographical Patterns
• A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
• $k$-POD: A Method for $k$-Means Clustering of Missing Data
• Distance Majorization and Its Applications
• Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
• A robust and sparse K-means clustering algorithm