Fuzzy Ontology for Distributed Document Clustering based on Genetic Algorithm

被引:0
作者
Thangamani, M. [1 ]
Thangaraj, P. [2 ]
机构
[1] Kongu Engn Coll, Dept Comp Sci & Engn, Erode 638052, Tamil Nadu, India
[2] Bannari Amman Inst Technol, Dept Comp Sci & Engn, Sathyamangalam, Tamil Nadu, India
来源
APPLIED MATHEMATICS & INFORMATION SCIENCES | 2013年 / 7卷 / 04期
关键词
Ontology; Genetic Algorithm; Document Clustering; Conceptual Clustering;
D O I
暂无
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The availability of large quantity of text documents from the World Wide Web and business document management systems has made the dynamic separation of texts into new categories as a very important task for every business intelligence systems. But, present text clustering algorithms still suffer from problems of practical applicability. Recent studies have shown that, in order to improve the performance of document clustering, ontologies are useful. Ontology is nothing but the conceptualization of a domain into an individual identifiable format, but machine-readable format containing entities, attributes, relationships and axioms. By analyzing all types of techniques for document clustering, a clustering technique depending on Genetic Algorithm (GA) is determined to be better as GA is a global convergence technique and has the ability of determining the most suitable cluster centers without difficulties. In this paper, a new document clustering scheme with fuzzy ontology based genetic clustering is proposed. The experimental results reveal that the proposed approach increases the accuracy to a large extent and the clustering time is also highly reduced.
引用
收藏
页码:1563 / 1574
页数:12
相关论文
共 41 条
[1]  
[Anonymous], 2012, Formal concept analysis: mathematical foundations
[2]   A recursive clustering methodology using a genetic algorithm [J].
Banerjee, Amit ;
Louis, Sushil J. .
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, :2165-2172
[3]   Partially supervised clustering for image segmentation [J].
Bensaid, AM ;
Hall, LO ;
Bezdek, JC ;
Clarke, LP .
PATTERN RECOGNITION, 1996, 29 (05) :859-871
[4]  
Berkhin P., 2002, SURVEY CLUSTERING DA
[5]  
Cao TH, 2008, IEEE INT CONF FUZZY, P2030
[6]  
Casillas A, 2003, LECT NOTES ARTIF INT, V2807, P43
[7]  
Cobos C., 2010, IEEE Congress on Evolutionary Computation (CEC), P1
[8]   Genetic algorithms for clustering in machine vision [J].
Cucchiara, R .
MACHINE VISION AND APPLICATIONS, 1998, 11 (01) :1-6
[9]  
Ding L., 2004, P 13 ACM INT C INF K, P652, DOI DOI 10.1145/1031171.1031289
[10]  
ELdesoky A. E., 2009, 2009 International Conference on Networking and Media Convergence (ICNM'09), P92, DOI 10.1109/ICNM.2009.4907196