A K-medoids clustering algorithm for mixed feature-type symbolic data
Elaine Cristina De Assis, Renata M.C.R. de Souza · 2011
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.