Title :
Algorithms for Clustering Terms in Document Set Based on Fuzzy Neighborhoods
Author :
Miyamoto, Sadaaki ; Kataoka, Erina
Author_Institution :
Dept. of Risk Eng., Tsukuba Univ.
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;
Conference_Titel :
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Conference_Location :
Reno, NV
Print_ISBN :
0-7803-9159-4
DOI :
10.1109/FUZZY.2005.1452527
