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Title:
A three-phase approach to document clustering based on topic significance degree
Author: Ma, Yinglong (1) ; Wang, Yao (1) ; Jin, Beihong (2)
Corresponding Author: Ma, Y.(yinglongma@gmail.com)
Keyword: Document clustering ; Topic model ; K-means ; K-means plus
Source: Expert Systems with Applications
Issued Date: 2014
Volume: 41, Issue:18, Pages:8203-8210
Indexed Type: SCI ; EI
Department: (1) School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China; (2) Technology Center of Software Engineering, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Abstract: Topic model can project documents into a topic space which facilitates effective document clustering. Selecting a good topic model and improving clustering performance are two highly correlated problems for topic based document clustering. In this paper, we propose a three-phase approach to topic based document clustering. In the first phase, we determine the best topic model and present a formal concept about significance degree of topics and some topic selection criteria, through which we can find the best number of the most suitable topics from the original topic model discovered by LDA. Then, we choose the initial clustering centers by using the k-means++ algorithm. In the third phase, we take the obtained initial clustering centers and use the k-means algorithm for document clustering. Three clustering solutions based on the three phase approach are used for document clustering. The related experiments of the three solutions are made for comparing and illustrating the effectiveness and efficiency of our approach. © 2014 Elsevier Ltd. All rights reserved.
English Abstract: Topic model can project documents into a topic space which facilitates effective document clustering. Selecting a good topic model and improving clustering performance are two highly correlated problems for topic based document clustering. In this paper, we propose a three-phase approach to topic based document clustering. In the first phase, we determine the best topic model and present a formal concept about significance degree of topics and some topic selection criteria, through which we can find the best number of the most suitable topics from the original topic model discovered by LDA. Then, we choose the initial clustering centers by using the k-means++ algorithm. In the third phase, we take the obtained initial clustering centers and use the k-means algorithm for document clustering. Three clustering solutions based on the three phase approach are used for document clustering. The related experiments of the three solutions are made for comparing and illustrating the effectiveness and efficiency of our approach. © 2014 Elsevier Ltd. All rights reserved.
Language: 英语
WOS ID: WOS:000342250300015
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16790
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Ma, Yinglong ,Wang, Yao ,Jin, Beihong . A three-phase approach to document clustering based on topic significance degree[J]. Expert Systems with Applications,2014-01-01,41(18):8203-8210.
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