Dirichlet Process Mixture Model for Document Clustering with Feature Partition
- Authors
- Huang, Ruizhang; Yu, Guan; Wang, Zhaojun; Zhang, Jun; Shi, Liangxing
- Issue Date
- Aug-2013
- Publisher
- Institute of Electrical and Electronics Engineers
- Keywords
- Database management; database applications-text mining; pattern recognition; clustering document clustering; Dirichlet process mixture model; feature partition
- Citation
- IEEE Transactions on Knowledge and Data Engineering, v.25, no.8, pp 1748 - 1759
- Pages
- 12
- Indexed
- SCI
SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Knowledge and Data Engineering
- Volume
- 25
- Number
- 8
- Start Page
- 1748
- End Page
- 1759
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115853
- DOI
- 10.1109/TKDE.2012.27
- ISSN
- 1041-4347
1558-2191
- Abstract
- Finding the appropriate number of clusters to which documents should be partitioned is crucial in document clustering. In this paper, we propose a novel approach, namely DPMFP, to discover the latent cluster structure based on the DPM model without requiring the number of clusters as input. Document features are automatically partitioned into two groups, in particular, discriminative words and nondiscriminative words, and contribute differently to document clustering. A variational inference algorithm is investigated to infer the document collection structure as well as the partition of document words at the same time. Our experiments indicate that our proposed approach performs well on the synthetic data set as well as real data sets. The comparison between our approach and state-of-the-art document clustering approaches shows that our approach is robust and effective for document clustering.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.