Simple and Scalable Sparse k-means Clustering via Feature Ranking

• URL: http://arxiv.org/abs/2002.08541v2
• Date: Thu, 22 Oct 2020 11:28:41 GMT
• Title: Simple and Scalable Sparse k-means Clustering via Feature Ranking
• Authors: Zhiyue Zhang, Kenneth Lange, Jason Xu
• Abstract summary: We propose a novel framework for sparse k-means clustering that is intuitive, simple to implement, and competitive with state-of-the-art algorithms. Our core method readily generalizes to several task-specific algorithms such as clustering on subsets of attributes and in partially observed data settings.
• Score: 14.839931533868176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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