A three-phase approach to document clustering based on topic significance degree

ISCAS OpenIR

	A three-phase approach to document clustering based on topic significance degree
	Ma, Yinglong (1); Wang, Yao (1); Jin, Beihong (2); Ma, Y.(yinglongma@gmail.com)
	2014
发表期刊	Expert Systems with Applications
ISSN	9574174
卷号	41 期号:18 页码:8203-8210
摘要	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.; 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.
收录类别	SCI ; EI
关键词	Document Clustering Topic Model K-means K-means Plus
部门归属	(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
语种	英语
WOS记录号	WOS:000342250300015
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16790
专题	中国科学院软件研究所
通讯作者	Ma, Y.(yinglongma@gmail.com)
推荐引用方式 GB/T 7714	Ma, Yinglong ,Wang, Yao ,Jin, Beihong ,et al. A three-phase approach to document clustering based on topic significance degree[J]. Expert Systems with Applications,2014,41(18):8203-8210.
APA	Ma, Yinglong ,Wang, Yao ,Jin, Beihong ,&Ma, Y..(2014).A three-phase approach to document clustering based on topic significance degree.Expert Systems with Applications,41(18),8203-8210.
MLA	Ma, Yinglong ,et al."A three-phase approach to document clustering based on topic significance degree".Expert Systems with Applications 41.18(2014):8203-8210.