A robust iterative refinement clustering algorithm with smoothing search space

  • Authors:

  • Yu Zong;Guandong Xu;Yanchun Zhang;He Jiang;Mingchu Li

  • Affiliations:

  • School of Software, Dalian University of Technology, Dalian 116621, China and School of Science and Engineering, Center for Applied Informatics, Victoria University, Melbourne VIC8001, Australia;School of Science and Engineering, Center for Applied Informatics, Victoria University, Melbourne VIC8001, Australia;School of Science and Engineering, Center for Applied Informatics, Victoria University, Melbourne VIC8001, Australia;School of Software, Dalian University of Technology, Dalian 116621, China;School of Software, Dalian University of Technology, Dalian 116621, China

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2010

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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.