Clustering of symbolic data through a dissimilarity volume based measure

Author

Silva, Kelly P. ; De Carvalho, Francisco A T ; Csernel, M.

Author_Institution

Center of Inf., Fed. Univ. of Pernambuco, Recife

fYear

2008

fDate

1-8 June 2008

Firstpage

2865

Lastpage

2871

Abstract

The recording of symbolic data has become a common practice with the advances in database technologies. This paper shows hard and fuzzy relational clustering in order to partition symbolic data. These methods optimize objective functions based on a dissimilarity function. The distance used is a volume based measure and may be applied to data described by set-valued, list-valued or interval-valued symbolic variables. Experiments with real and synthetic symbolic data sets show the usefulness of the proposed approach.

Keywords

fuzzy set theory; pattern clustering; relational databases; database technologies; dissimilarity function; dissimilarity volume based measurement; fuzzy relational clustering; interval-valued symbolic variables; list-valued symbolic variables; set-valued symbolic variables; symbolic data clustering; symbolic data partitioning; symbolic data recording; Clustering algorithms; Clustering methods; Data analysis; Data mining; Heuristic algorithms; Iterative algorithms; Partitioning algorithms; Pattern analysis; Prototypes; Volume measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Conference_Location

Hong Kong

ISSN

1098-7576

Print_ISBN

978-1-4244-1820-6

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2008.4634201

Filename

4634201