Fuzzy clustering and categorization of text documents

Author

Ayeldeen, Heba ; Hassanien, Aboul Ella ; Fahmy, Aly A.

Author_Institution

Fac. of Comput. & Inf., Cairo Univ., Cairo, Egypt

fYear

2013

Firstpage

262

Lastpage

266

Abstract

The fuzzy Euclidean distance clustering algorithm has been well studied and used in information retrieval society for clustering documents. However, the fuzzy logic algorithm poses problems in dealing with large amount of data. In this paper we proposed results for clustering theses documents based on Euclidean distances and cluster-dependent keyword weighting. The proposed approach is based on the Fuzzy Euclidean distance clustering algorithm. The cluster dependent keyword weighting help in partitioning and categorizing the theses documents into more meaningful categories.

Keywords

data mining; document handling; fuzzy logic; medical computing; ontologies (artificial intelligence); pattern clustering; cluster-dependent keyword weighting; document clustering; fuzzy Euclidean distance clustering algorithm; fuzzy logic algorithm; information retrieval; text document categorization; Biopsy; Catheters; Decision support systems; Ontologies; Pacemakers; Surgery; Ultrasonic imaging; Fuzzy Euclidean distance Algorithm; Lexical similarity; MeSH; Medical Ontology;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems (HIS), 2013 13th International Conference on

Conference_Location

Gammarth

Print_ISBN

978-1-4799-2438-7

Type

conf

DOI

10.1109/HIS.2013.6920493

Filename

6920493