Quantization-based clustering algorithm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
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Author(s)

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Detail(s)

Original languageEnglish
Pages (from-to)2698-2711
Journal / PublicationPattern Recognition
Volume43
Issue number8
Publication statusPublished - Aug 2010

Abstract

In this paper, a quantization-based clustering algorithm (QBCA) is proposed to cluster a large number of data points efficiently. Unlike previous clustering algorithms, QBCA places more emphasis on the computation time of the algorithm. Specifically, QBCA first assigns the data points to a set of histogram bins by a quantization function. Then, it determines the initial centers of the clusters according to this point distribution. Finally, QBCA performs clustering at the histogram bin level, rather than the data point level. We also propose two approaches to improve the performance of QBCA further: (i) a shrinking process is performed on the histogram bins to reduce the number of distance computations and (ii) a hierarchical structure is constructed to perform efficient indexing on the histogram bins. Finally, we analyze the performance of QBCA theoretically and experimentally and show that the approach: (1) can be easily implemented, (2) identifies the clusters effectively and (3) outperforms most of the current state-of-the-art clustering approaches in terms of efficiency. © 2010 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Clustering algorithm, Histogram, K-means

Citation Format(s)

Quantization-based clustering algorithm. / Yu, Zhiwen; Wong, Hau-San.
In: Pattern Recognition, Vol. 43, No. 8, 08.2010, p. 2698-2711.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review