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A New Method for User Dynamic Clustering Based On HSMM in Model of SaaS |
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PP: 1059-1064 |
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Author(s) |
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ChunHua Ju,
Chonghuan Xu,
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Abstract |
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| This paper deeply studies the phenomenon of hard to satisfy the user’s personalized services and only a few researches
on users themselves in the model of Software as a Service (SaaS), then proposes a users’ behavior feature extraction model based on
Hidden Semi-Markov Models (HSMM) to solve the problem of getting users hidden information on SaaS platform first. The model uses
the probability distribution of state duration time to control user’s browsing behaviors, combines hidden states which describe features
with time relativity, and applies improved Viterbi algorithm to get user features sequence. Then cluster users by dynamic K-means
algorithm, which doesn’t need to give K cluster centers in the process of clustering but adjusts center value automatically through
the comparison of clustering quality in every iterative process, finally gets optimal clustering results. Detailed simulation analysis
demonstrates that the presented algorithm is of high efficiency of space and time and is more stable |
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