Ensemble of Multi-objective Clustering Unified with H-Confidence Metric as Validity Metric

Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall, ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical, further on mixed credit card dataset with different objectives, and compared them against single objective clustering result in terms of purity.

[1]  Tansel Özyer,et al.  Association-Rules Mining Based Broadcasting Approach for XML Data , 2006, ADVIS.

[2]  Tansel Özyer,et al.  Deciding on Number of Clusters by Multi-Objective Optimization and Validity Analysis , 2008, J. Multiple Valued Log. Soft Comput..

[3]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[4]  Tansel Özyer,et al.  Clustering by Integrating Multi-objective Optimization with Weighted K-Means and Validity Analysis , 2006, IDEAL.

[5]  Tansel Özyer,et al.  Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer , 2009, Applied Intelligence.

[6]  Huaning Yan.  Determining the Best K for Clustering Transactional Datasets : A Coverage Density-based Approach , 2008 .

[7]  R. Alhajj,et al.  Achieving Natural Clustering by Validating Results of Iterative Evolutionary Clustering Approach , 2006, 2006 3rd International IEEE Conference Intelligent Systems.

[8]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[9]  Reda Alhajj,et al.  COMBINING VALIDITY INDEXES AND MULTI-OBJECTIVE OPTIMIZATION BASED CLUSTERING , 2006 .

[10]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[11]  K. Thangavel,et al.  Improved K-Modes for Categorical Clustering Using Weighted Dissimilarity Measure , 2009 .

[12]  Hui Xiong,et al.  Hyperclique pattern discovery , 2006, Data Mining and Knowledge Discovery.

[13]  Joshua Zhexue Huang,et al.  A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining , 1997, DMKD.