BV-RSA: A rapid simulated annealing model for ensemble clustering

There are two key issues in applying simulated annealing method to solve the problem of ensemble clustering. One is improving the solution quality as much as possible, the other is accelerating the annealing process, thus obtain the solution rapidly. Aiming at solving the two questions, a rapid simulated annealing model for ensemble clustering, called BV-RSA, is presented. In BV-RSA, the partial consensus of basic partitions is used as important heuristic information, data objects with consensus cluster label in basic partitions are controlled moving in a group way, and their moving directions are decided by the positive-negative voting, thus reduce the randomness of object moving and speed up the clustering behavior in annealing process. Experiments on real world data set demonstrate that under any initial state, BV-RSA model performance well both in convergence and robustness.

[1]  Mohamed S. Kamel,et al.  Refined Shared Nearest Neighbors Graph for Combining Multiple Data Clusterings , 2003, IDA.

[2]  Zhiwu Lu,et al.  From comparing clusterings to combining clusterings , 2008, AAAI 2008.

[3]  Rich Caruana,et al.  Consensus Clusterings , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[4]  Hui Xiong,et al.  A Theoretic Framework of K-Means-Based Consensus Clustering , 2013, IJCAI.

[5]  Junjie Wu K-means Based Consensus Clustering , 2012 .

[6]  Ana L. N. Fred,et al.  Probabilistic consensus clustering using evidence accumulation , 2013, Machine Learning.

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

[8]  Mohamed S. Kamel,et al.  An aggregated clustering approach using multi-ant colonies algorithms , 2006, Pattern Recognit..

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

[10]  Mohamed S. Kamel,et al.  Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Hui Xiong,et al.  K-Means-Based Consensus Clustering: A Unified View , 2015, IEEE Transactions on Knowledge and Data Engineering.