DocumentCode
2228121
Title
Analyzing Distance Measures for Symbolic Data Based on Fuzzy Clustering
Author
Da Silva, Alzennyr ; Lechevallier, Yves ; de Carvalho, Fausto
Author_Institution
Project AxIS, Le Chesnay
fYear
2007
fDate
20-24 Oct. 2007
Firstpage
109
Lastpage
114
Abstract
Various propositions to solve the problem of symbolic data clustering are available in the literature. This paper introduces a comparative study among some well known dissimilarity functions treating symbolic data. An extension of the fuzzy c-means clustering algorithm is used to create groups of individuals characterized by symbolic variables of mixed types. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion dependent on the dissimilarity function. Experiments involving benchmark data sets are carried out in order to compare the accuracy of each function.
Keywords
fuzzy set theory; pattern clustering; distance measure analysis; fuzzy c-means clustering algorithm; fuzzy partition; symbolic data clustering; Clustering algorithms; Data analysis; Deductive databases; Fuzzy systems; Heuristic algorithms; Intelligent systems; Iterative algorithms; Optimization methods; Partitioning algorithms; Prototypes;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on
Conference_Location
Rio de Janeiro
Print_ISBN
978-0-7695-2976-9
Type
conf
DOI
10.1109/ISDA.2007.52
Filename
4389594
Link To Document