Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2018

Abstract

Due to its simplicity and versatility, k-means remains popular since it was proposed three decades ago. The performance of k-means has been enhanced from different perspectives over the years. Unfortunately, a good trade-off between quality and efficiency is hardly reached. In this paper, a novel k-means variant is presented. Different from most of k-means variants, the clustering procedure is driven by an explicit objective function, which is feasible for the whole l(2)-space. The classic egg-chicken loop in k-means has been simplified to a pure stochastic optimization procedure. The procedure of k-means becomes simpler and converges to a considerably better local optima. The effectiveness of this new variant has been studied extensively in different contexts, such as document clustering, nearest neighbor search and image clustering. Superior performance is observed across different scenarios. (c) 2018 Elsevier B.V. All rights reserved.

Keywords

Clustering, k-means, Incremental optimization

Discipline

Computer Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Neurocomputing

Volume

291

First Page

195

Last Page

206

ISSN

0925-2312

Identifier

10.1016/j.neucom.2018.02.072

Publisher

Elsevier

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