A K-means-based Algorithm for Projective Clustering

In this paper, a new algorithm for projective clustering is proposed. The algorithm consists of two phases. The first phase performs attribute relevance analysis by detecting dense regions in each attribute, thereby allowing irrelevant attributes and outliers to be captured and eliminated. Starting...

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Bibliographic Details
Published in18th International Conference on Pattern Recognition (ICPR'06) Vol. 1; pp. 888 - 891
Main Authors Bouguessa, M., Shengrui Wang, Qingshan Jiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Clustering algorithms
Clustering methods
Computer science
Data mining
Gaussian distribution
Pattern recognition
Performance analysis
Phase detection
Software algorithms
Software measurement
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ISBN0769525210
9780769525211
ISSN1051-4651
DOI10.1109/ICPR.2006.88

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Summary:In this paper, a new algorithm for projective clustering is proposed. The algorithm consists of two phases. The first phase performs attribute relevance analysis by detecting dense regions in each attribute, thereby allowing irrelevant attributes and outliers to be captured and eliminated. Starting from the results of the first phase, the second phase aims to uncover clusters in different subspaces. The clustering process is based on the k-means algorithm, with the computation of distance restricted to subsets of attributes where object values are dense
ISBN:0769525210
9780769525211
ISSN:1051-4651
DOI:10.1109/ICPR.2006.88