A Robust Algorithm for Subspace Clustering of High-Dimensional Data*

Research Article

A Robust Algorithm for Subspace Clustering of High-Dimensional Data*

Hongfang Zhou, Boqin Feng, Lintao Lv and Yue Hui

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.

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Hongfang Zhou, Boqin Feng, Lintao Lv and Yue Hui, 2007. A Robust Algorithm for Subspace Clustering of High-Dimensional Data*. Information Technology Journal, 6: 255-258.

DOI: 10.3923/itj.2007.255.258

URL: https://scialert.net/abstract/?doi=itj.2007.255.258

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Authors

Hongfang Zhou

Boqin Feng

Lintao Lv

Yue Hui

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Research Article

A Robust Algorithm for Subspace Clustering of High-Dimensional Data*

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