The b-chromatic number of a graph
Discrete Applied Mathematics
ACM Computing Surveys (CSUR)
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualization of navigation patterns on a Web site using model-based clustering
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Time series similarity measures (tutorial PM-2)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A new clustering approach for symbolic data and its validation: application to the healthcare data
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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This paper proposes a new integrated sequential data clustering framework based on an iterative process which alternates between the EM process and a modified b-coloring clustering algorithm. It exhibits two important features: Firstly, the proposed framework allows to give an assignment of clusters to the sequences where the b-coloring properties are maintained as long as the clustering process runs. Secondly, it gives each cluster a twofold representation by a generative model (Markov chains) as well as dominant members which ensure the global stability of the returned partition. The proposed framework is evaluated against benchmark datasets in UCI repository and its effectiveness is confirmed.