Login New user?  
01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 
 

Content
 

Volumes > Volume 07 > No. 3

 
   

A New Method for User Dynamic Clustering Based On HSMM in Model of SaaS

PP: 1059-1064
Author(s)
ChunHua Ju, Chonghuan Xu,
Abstract
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

  Home   About us   News   Journals   Conferences Contact us Copyright naturalspublishing.com. All Rights Reserved