A robust algorithm for subspace clustering of high-dimensional data.

Hongfang Zhou; Boqin Feng; Yue Hui; L. V. Lintao

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A robust algorithm for subspace clustering of high-dimensional data.

Author(s)

Hongfang Zhou School of Electronics and Information Engineering, Xi’an Jiaotong University, Xian, Shaanxi, China

Boqin Feng School of Electronics and Information Engineering, Xi’an Jiaotong University, Xian, Shaanxi, China

Yue Hui School of Electronics and Information Engineering, Xi’an Jiaotong University, Xian, Shaanxi, China

L. V. Lintao School of Computer Science and Engineering, Xi’an Jiaotogn University, of Technology, Xi’an, Shaanxi, China

Abstract

Subspace clustering has been studied extensively and widely since traditional algorithms are ineffective in high-dimensional data spaces. Firstly, they were sensitive to noises, which are inevitable in high-dimensional data spaces; secondly, they were too severely dependent on some distance metrics, which cannot act as virtual indicators as in high-dimensional data spaces; thirdly, they often use a global threshold, but different groups of features behave differently in various dimensional subspaces. Accordingly, traditional clustering algorithms are not suitable in high-dimensional spaces. On the analysis of the advantages and disadvantages inherent to the traditional clustering algorithm, we propose a robust algorithm JPA (Joining-Pruning Algorithm). Our algorithm is based on an efficient two-phase architecture. The experiments show that our algorithm achieves a significant gain of runtime and quality in comparison to nowadays subspace clustering algorithms.

Publication Details

Page(s) 255-258

DOI DOI not available

Published Journal: Information technology Journal, Volume: 6, Issue: 2, Year: 2007

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