Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Scalable Construction of Topic Directory with Nonparametric Closed Termset Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
High Quality, Efficient Hierarchical Document Clustering Using Closed Interesting Itemsets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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
Novel data stream pattern mining report on the StreamKDD'10 workshop
ACM SIGKDD Explorations Newsletter
Spatio-temporal data evolutionary clustering based on MOEA/D
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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Evolutionary clustering is an emerging research area addressing the problem of clustering dynamic data. An evolutionary clustering should take care of two conflicting criteria: preserving the current cluster quality and not deviating too much from the recent history. In this paper we propose an algorithm for evolutionary clustering using frequent itemsets. A frequent itemset based approach for evolutionary clustering is natural and it automatically satisfy the two criteria of evolutionary clustering. We provide theoretical as well as experimental proofs to support our claims. We performed experiments on our approach using different datasets and the results show that our approach is comparable to most of the existing algorithms for evolutionary clustering.