A three-phase approach to document clustering based on topic significance degree
文献类型：期刊论文
| 作者 | Ma, Yinglong (1) ; Wang, Yao (1) ; Jin, Beihong (2) |
| 刊名 | Expert Systems with Applications
 |
| 出版日期 | 2014 |
| 卷号 | 41期号:18页码:8203-8210 |
| 关键词 | Document clustering Topic model K-means K-means plus |
| ISSN号 | 9574174 |
| 通讯作者 | Ma, Y.(yinglongma@gmail.com) |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000342250300015 |
| 公开日期 | 2014-12-16 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16790]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Ma, Yinglong ,Wang, Yao ,Jin, Beihong . 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 .(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. |
入库方式： OAI收割
来源：软件研究所
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