A Novel Clustering Technique Based on Improved Noising Method

In this article, the clustering problem under the criterion of minimum sum of squares clustering is considered. It is known that this problem is a nonconvex program which possesses many locally optimal values, resulting that its solution often falls into these traps. To explore the proper result, a novel clustering technique based on improved noising method called INMC is developed, in which one-step DHB algorithm as the local improvement operation is integrated into the algorithm framework to fine-tune the clustering solution obtained in the process of iterations. Moreover, a new method for creating the neighboring solution of the noising method called mergence and partition operation is designed and analyzed in detail. Compared with two noising method based clustering algorithms recently reported, the proposed algorithm greatly improves the performance without the increase of the time complexity, which is extensively demonstrated for experimental data sets.

[1]  C. A. Murthy,et al.  In search of optimal clusters using genetic algorithms , 1996, Pattern Recognit. Lett..

[2]  Yan Liu,et al.  Clustering with Noising Method , 2005, ADMA.

[3]  Shokri Z. Selim,et al.  K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Patrick Siarry,et al.  Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions , 2003, Eur. J. Oper. Res..

[5]  Yi-tzuu T. Chien.  Interactive pattern recognition , 1978 .

[6]  Qiwen Zhang,et al.  A new clustering algorithm with multiple runs of iterative procedures , 1991, Pattern Recognit..

[7]  Dong-Jo Park,et al.  A Novel Validity Index for Determination of the Optimal Number of Clusters , 2001 .

[8]  Irène Charon,et al.  The noising methods: A generalization of some metaheuristics , 2001, Eur. J. Oper. Res..

[9]  SANGHAMITRA BANDYOPADHYAY,et al.  Clustering Using Simulated Annealing with Probabilistic Redistribution , 2001, Int. J. Pattern Recognit. Artif. Intell..

[10]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[11]  Patrick Siarry,et al.  A hybrid method combining continuous tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions , 2005, Eur. J. Oper. Res..

[12]  Khaled S. Al-Sultan,et al.  A Tabu search approach to the clustering problem , 1995, Pattern Recognit..

[13]  M. Narasimha Murty,et al.  Clustering with evolution strategies , 1994, Pattern Recognit..

[14]  Mohamed A. Ismail,et al.  Multidimensional data clustering utilizing hybrid search strategies , 1989, Pattern Recognit..

[15]  P. Brucker.  On the Complexity of Clustering Problems , 1978 .

[16]  Irène Charon,et al.  The noising method: a new method for combinatorial optimization , 1993, Oper. Res. Lett..

[17]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.