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A robust iterative refinement clustering algorithm with smoothing search space

Zong, Yu, Xu, Guandong, Zhang, Yanchun, Jiang, He and Li, Mingchu (2010) A robust iterative refinement clustering algorithm with smoothing search space. Knowledge-Based Systems, 23 (5). pp. 389-396. ISSN 0950-7051

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Abstract

Iterative refinement clustering algorithms are widely used in data mining area, but they are sensitive to the initialization. In the past decades, many modified initialization methods have been proposed to reduce the influence of initialization sensitivity problem. The essence of iterative refinement clustering algorithms is the local search method. The big numbers of the local minimum points which are embedded in the search space make the local search problem hard and sensitive to the initialization. The smaller number of local minimum points, the more robust of initialization for a local search algorithm is. In this paper, we propose a Top–Down Clustering algorithm with Smoothing Search Space (TDCS3) to reduce the influence of initialization. The main steps of TDCS3 are to: (1) dynamically reconstruct a series of smoothed search spaces into a hierarchical structure by ‘filling’ the local minimum points; (2) at the top level of the hierarchical structure, an existing iterative refinement clustering algorithm is run with random initialization to generate the clustering result; (3) eventually from the second level to the bottom level of the hierarchical structure, the same clustering algorithm is run with the initialization derived from the previous clustering result. Experiment results on 3 synthetic and 10 real world data sets have shown that TDCS3 has significant effects on finding better, robust clustering result and reducing the impact of initialization.

Item Type: Article
Uncontrolled Keywords: ResPubID19693, clustering, smoothing search space, kernel smoothing, heuristic algorithm
Subjects: FOR Classification > 0802 Computation Theory and Mathematics
SEO Classification > 970108 Expanding Knowledge in the Information and Computing Sciences
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 15 Jun 2012 06:14
Last Modified: 20 Jun 2012 04:40
URI: http://vuir.vu.edu.au/id/eprint/7373
DOI: https://doi.org/10.1016/j.knosys.2010.01.012
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Citations in Scopus: 4 - View on Scopus

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