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
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)
Online video segmentation by bayesian split-merge clustering
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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
Real time event detection in twitter
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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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Evolutionary Clustering has emerged as an important research topic in recent literature of data mining, and solutions to this problem have found a wide spectrum of applications, particularly in social network analysis. In this paper, based on the recent literature on Dirichlet processes, we have developed two different and specific models as solutions to this problem: DPChain and HDP-EVO. Both models substantially advance the literature on evolutionary clustering in the sense that not only they both perform better than the existing literature, but more importantly they are capable of automatically learning the cluster numbers and structures during the evolution. Extensive evaluations have demonstrated the effectiveness and promise of these models against the state-of-the-art literature.