Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Dirichlet Process Based Evolutionary Clustering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Evolutionary Clustering by Hierarchical Dirichlet Process with Hidden Markov State
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary clustering using frequent itemsets
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
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Clustering the data evolve with time, which is termed evolutionary clustering, is an emerging and important research area in recent literature of data mining, and it is very effective to cluster the dynamic data. It needs to consider two conflicting criteria. One is the snapshot quality function; the other is the history cost function. Most state-of-the-art methods combine these two objectives into one and apply a single objective optimization method for optimizing it. In this paper, we propose a new evolutionary clustering approach by using a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to optimize these two conflicting functions in evolutionary k-means algorithm (EKM). The experimental results demonstrate that our algorithm significantly outperforms EKM.