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A Robust Iterative Refinement Clustering Algorithm with Smoothing Search Space by Yu Zong; Guandong Xu; Yanchun Zhang; He Jiang; Mingchu Li is a Computer Science article available to read on EtoBox.

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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 clusterin

Who reads A Robust Iterative Refinement Clustering Algorithm with Smoothing Search Space?

Author

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

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0950-7051)

Published

2010

Language

EN

Field

Computer Science (Physical Sciences)