Community evolution detection in dynamic heterogeneous information networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Evolutionary clustering using frequent itemsets
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Towards mobility-based clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatio-temporal data evolutionary clustering based on MOEA/D
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Generative Models for Evolutionary Clustering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining evolutionary multi-branch trees from text streams
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 17th International Database Engineering & Applications Symposium
Dynamic joint sentiment-topic model
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Adaptive evolutionary clustering
Data Mining and Knowledge Discovery
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This paper studies evolutionary clustering, which is a recently hot topic with many important applications, noticeably in social network analysis. In this paper, based on the recent literature on Hierarchical Dirichlet Process (HDP) and Hidden Markov Model (HMM), we have developed a statistical model HDP-HTM that combines HDP with a Hierarchical Transition Matrix (HTM) based on the proposed Infinite Hierarchical Hidden Markov State model (iH$^2$MS) as an effective solution to this problem. The HDP-HTM model substantially advances the literature on evolutionary clustering in the sense that not only it performs better than the existing literature, but more importantly it is capable of automatically learning the cluster numbers and structures and at the same time explicitly addresses the correspondence issue during the evolution. Extensive evaluations have demonstrated the effectiveness and promise of this solution against the state-of-the-art literature.