Paper Info
Title
Dual Fuzzy-Possibilistic Co-clustering for Document Categorization
Abstract
In this paper, we introduce a new algorithm called Dual Fuzzy-possibilistic Co-clustering (DFPC) for docu- ment categorization. The proposed algorithm offers several advantages. Firstly, the combined fuzzy and possibilistic cluster memberships in DFPC can provide realistic repre- sentation of document clusters. Secondly, as a co-clustering algorithm, DFPC can categorize high-dimensional datasets effectively. Thirdly, the possibilistic clustering element of the algorithm makes it robust to outliers. We detail the for- mulation of DFPC, and empirically demonstrate its effec- tiveness in categorizing benchmark document datasets.
Year
DOI
Venue
2007
10.1109/ICDMW.2007.37
ICDM Workshops
Keywords
Field
DocType
dual fuzzy-possibilistic co-clustering,high-dimensional datasets,document cluster,possibilistic clustering element,document categorization,new algorithm,proposed algorithm,ment categorization,possibilistic cluster membership,benchmark document datasets,co-clustering algorithm,constraint optimization,tv,context modeling,clustering algorithms,fuzzy sets,data mining,robustness,matrix decomposition,data engineering,document clustering
Data mining,Computer science,Context model,Robustness (computer science),Fuzzy set,Information engineering,Artificial intelligence,Biclustering,Cluster analysis,Categorization,Pattern recognition,Fuzzy logic,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7695-3033-8
24
1.29
References 
Authors
14
2
Name
Order
Citations
PageRank
William-Chandra Tjhi115610.09
Lihui Chen2283.76