Title :
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
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;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634201
