A novel attribute weighting algorithm for clustering high-dimensional categorical data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

90 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2843-2861
Journal / PublicationPattern Recognition
Volume44
Issue number12
Publication statusPublished - Dec 2011

Abstract

Due to data sparseness and attribute redundancy in high-dimensional data, clusters of objects often exist in subspaces rather than in the entire space. To effectively address this issue, this paper presents a new optimization algorithm for clustering high-dimensional categorical data, which is an extension of the k-modes clustering algorithm. In the proposed algorithm, a novel weighting technique for categorical data is developed to calculate two weights for each attribute (or dimension) in each cluster and use the weight values to identify the subsets of important attributes that categorize different clusters. The convergence of the algorithm under an optimization framework is proved. The performance and scalability of the algorithm is evaluated experimentally on both synthetic and real data sets. The experimental studies show that the proposed algorithm is effective in clustering categorical data sets and also scalable to large data sets owning to its linear time complexity with respect to the number of data objects, attributes or clusters. © 2011 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Attribute weighting, Cluster analysis, High-dimensional categorical data, Optimization algorithm, Subspace clustering

Citation Format(s)

A novel attribute weighting algorithm for clustering high-dimensional categorical data. / Bai, Liang; Liang, Jiye; Dang, Chuangyin et al.
In: Pattern Recognition, Vol. 44, No. 12, 12.2011, p. 2843-2861.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review