Combining Multiple Clusterings Via k-Modes Algorithm

Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the k-modes algorithm is proposed in this paper. A combined partition is found as a solution to the corresponding categorical data clustering problem using the k-modes algorithm. This study compares the performance of the k-modes consensus algorithm with other fusion approaches for clustering ensembles. Experimental results demonstrate the effectiveness of the proposed method.

[1]  G DietterichThomas.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees , 2000 .

[2]  Anil K. Jain,et al.  Combining multiple weak clusterings , 2003, Third IEEE International Conference on Data Mining.

[3]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[4]  William F. Punch,et al.  A Comparison of Resampling Methods for Clustering Ensembles , 2004, IC-AI.

[5]  Ana L. N. Fred,et al.  Data clustering using evidence accumulation , 2002, Object recognition supported by user interaction for service robots.

[6]  Anil K. Jain,et al.  Clustering ensembles: models of consensus and weak partitions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[8]  Joachim M. Buhmann,et al.  Bagging for Path-Based Clustering , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[10]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[11]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[12]  Carla E. Brodley,et al.  Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach , 2003, ICML.

[13]  Anil K. Jain,et al.  A Mixture Model for Clustering Ensembles , 2004, SDM.

[14]  Ana L. N. Fred,et al.  Finding Consistent Clusters in Data Partitions , 2001, Multiple Classifier Systems.

[15]  Douglas H. Fisher,et al.  Knowledge Acquisition Via Incremental Conceptual Clustering , 1987, Machine Learning.

[16]  Shashi Shekhar,et al.  Multilevel hypergraph partitioning: application in VLSI domain , 1997, DAC.

[17]  Joachim M. Buhmann,et al.  Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  William F. Punch,et al.  Ensembles of partitions via data resampling , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

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

[20]  Sandrine Dudoit,et al.  Bagging to Improve the Accuracy of A Clustering Procedure , 2003, Bioinform..

[21]  Anil K. Jain,et al.  Multiobjective data clustering , 2004, CVPR 2004.