A K-medoids clustering algorithm for mixed feature-type symbolic data

Author

De Assis, Elaine Cristina ; De Souza, Renata M C R

Author_Institution

Comput. Sci. Center, Fed. Univ. of Pernambuco, UFPE, Recife, Brazil

fYear

2011

fDate

9-12 Oct. 2011

Firstpage

527

Lastpage

531

Abstract

A K-medoids clustering algorithm for mixed feature-type symbolic data represented by categorical, interval-valued and histogram-valued is presented in this paper. The algorithm furnishes a partition and a prototype to each class by optimizing an adequacy criterion based on a suitable standardized Euclidean distance. To evaluate the proposed algorithm, several real symbolic data sets are considered and the results furnished by this algorithm are compared with the results furnished by a partitional algorithm for mixed feature-type symbolic data of the literature of symbolic data analysis in terms of the correct Rand index.

Keywords

data analysis; pattern clustering; Rand index; adequacy criterion optimization; categorical interval-valued; histogram-valued; k-medoids clustering algorithm; mixed feature-type symbolic data; partitional algorithm; standardized Euclidean distance; symbolic data analysis; Algorithm design and analysis; Clustering algorithms; Clustering methods; Heuristic algorithms; Indexes; Partitioning algorithms; Temperature distribution; K-medoids clustering algorithm; mixed feature-type symbolic data; standardized Euclidean distance; symbolic data analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1062-922X

Print_ISBN

978-1-4577-0652-3

Type

conf

DOI

10.1109/ICSMC.2011.6083737

Filename

6083737