Algorithms for Clustering Terms in Document Set Based on Fuzzy Neighborhoods

Author

Miyamoto, Sadaaki ; Kataoka, Erina

Author_Institution

Dept. of Risk Eng., Tsukuba Univ.

fYear

2005

fDate

25-25 May 2005

Firstpage

979

Lastpage

984

Abstract

This paper describes similarity measures between two terms in a document set using the concept of a fuzzy neighborhood and algorithms for term clustering. Theoretical properties of neighborhood and similarity measures are studied. Agglomerative hierarchical as well as fuzzy/crisp c-means clustering algorithms are proposed. Examples of agglomerative and c-means clustering are given

Keywords

fuzzy set theory; pattern clustering; text analysis; agglomerative hierarchical clustering; crisp c-means clustering; document term clustering; fuzzy c-means clustering; fuzzy neighborhoods; similarity measures; Clustering algorithms; Electronic mail; Engines; Frequency measurement; Fuzzy sets; Fuzzy systems; Information retrieval; Web pages;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Conference_Location

Reno, NV

Print_ISBN

0-7803-9159-4

Type

conf

DOI

10.1109/FUZZY.2005.1452527

Filename

1452527

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3402286