Abstract
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.
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Elghazel, H., Yoshida, T., Hacid, MS. (2008). An Integrated Graph and Probability Based Clustering Framework for Sequential Data. In: Jean-Fran, JF., Berthold, M.R., Horváth, T. (eds) Discovery Science. DS 2008. Lecture Notes in Computer Science(), vol 5255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88411-8_24
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DOI: https://doi.org/10.1007/978-3-540-88411-8_24
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