Document Clustering Based on Probabilistic Topic Model

WANG Li-dong; WEI Bao-gang; YUAN Jie

doi:10.3969/j.issn.0372-2112.2012.11.033

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Document Clustering Based on Probabilistic Topic Model

更新时间：2025-07-16

- Document Clustering Based on Probabilistic Topic Model
- Acta Electronica Sinica Vol. 40, Issue 11, Pages: 2346-2350(2012)
- 作者机构：
  
  1. 浙江大学计算机科学与技术学院,浙江,杭州,310027
  2. 杭州师范大学,浙江,杭州,310012
  3. 浙江大学计算机科学与技术学院浙江杭州,310027
  4. 杭州师范大学浙江杭州,310012
- 作者简介：
- 基金信息：
  
  Youth Fund of National Natural Science Foundation of China (No.61103171, No.61103099);Non-profit Technology Application Research Project of Zhejiang Province (No.2011C31048)
- DOI：10.3969/j.issn.0372-2112.2012.11.033
  CLC： TP391
- Published：2012
- 稿件说明：
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WANG Li-dong, WEI Bao-gang, YUAN Jie. Document Clustering Based on Probabilistic Topic Model[J]. Acta Electronica Sinica, 2012, 40(11): 2346-2350.
DOI：

WANG Li-dong, WEI Bao-gang, YUAN Jie. Document Clustering Based on Probabilistic Topic Model[J]. Acta Electronica Sinica, 2012, 40(11): 2346-2350. DOI： 10.3969/j.issn.0372-2112.2012.11.033.

摘要

为了实现普通文本语料库和数字图书语料库的有效聚类

分别提出基于传统LDA(Latent Dirichlet Allocation)模型和TC_LDA模型的聚类算法.TC_LDA模型在LDA模型基础上进行扩展

通过对图书文档的目录和正文信息联合进行主题建模.和传统方法不同

基于主题模型的聚类算法能将具备同一主题的文档聚为一类.实验结果表明从主题分析角度出发实现的聚类算法优于传统的聚类算法.

Abstract

To effectively cluster corpus of ordinary documents and digital books

the clustering algorithms based on LDA model and TC_LDA were proposed

respectively.The topic model named TC_LDA

the extension of LDA

is proposed for digital books corpus for jointly topic modeling from both of Texts and Contents.Unlike traditional clustering methods

topic model based methods cluster documents in a group if they share one or more common topics.Empirical evaluation demonstrates that our approach based on topic analysis can substantially improve the clustering results as compared to related methods.

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