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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2012

01.03.2012 | Original Article

Soft subspace clustering with an improved feature weight self-adjustment mechanism

verfasst von: Gongde Guo, Si Chen, Lifei Chen

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2012

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Abstract

Traditional clustering algorithms are often defeated by high dimensionality. In order to find clusters hiding in different subspaces, soft subspace clustering has become an effective means of dealing with high dimensional data. However, most existing soft subspace clustering algorithms contain parameters which are difficult to be determined by users in real-world applications. A new soft subspace clustering algorithm named SC-IFWSA is proposed, which uses an improved feature weight self-adjustment mechanism IFWSA to update adaptively the weights of all features for each cluster according to the importance of the features to clustering quality and does not require users to set any parameter values. In addition, SC-IFWSA can overcome the traditional FWSA mechanism which may fail to calculate feature weights in some particular cases. In comparison with its related approaches, the experimental results carried out on ten data sets demonstrate the effectiveness and feasibility of the proposed method.
Vorheriger Artikel The design and dynamic analysis of a novel 6-DOF parallel mechanism
Nächster Artikel Noise reduction in microarray gene expression data based on spectral analysis

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Metadaten
Titel
Soft subspace clustering with an improved feature weight self-adjustment mechanism
verfasst von
Gongde Guo
Si Chen
Lifei Chen
Publikationsdatum
01.03.2012
Verlag
Springer-Verlag
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2012
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0038-8

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