Automatic keyword extraction with relational clustering and Levenshtein distances

Author

Runkler, Thomas A. ; Bezdek, James C.

Author_Institution

Corp. Technol., Siemens AG, Munich, Germany

Volume

2

fYear

2000

fDate

2000

Firstpage

636

Abstract

Alternating cluster estimation (ACE) is a generalized clustering model. Relational ACE is a modification of ACE that can be used to cluster data which do not possess a clear numerical representation, but for which a meaningful relation matrix can be defined. For text data sets we define (pairwise) relation matrices based on the Levenshtein string distance (1966). Relational ACE with Levenshtein distances is applied to four different texts. The cluster centers represent typical words in the texts, so this algorithm can be used to automatically determine keywords

Keywords

matrix algebra; pattern clustering; text analysis; ACE; Levenshtein distances; Levenshtein string distance; alternating cluster estimation; automatic keyword extraction; generalized clustering model; numerical representation; pairwise relation matrices; relation matrix; relational clustering; Clustering algorithms; Communications technology; Computer science; Data analysis; Data mining; Image sensors; Image sequence analysis; Optimization methods; Pixel; Prototypes;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on

Conference_Location

San Antonio, TX

ISSN

1098-7584

Print_ISBN

0-7803-5877-5

Type

conf

DOI

10.1109/FUZZY.2000.839067

Filename

839067