A partitioning method for mixed feature-type symbolic data using a squared Euclidean distance

Authors:
Renata Maria Cardoso Rodrigues de Souza;Francisco de Assis Tenorio de Carvalho;Daniel F. Pizzato
Affiliations:
Centro de Informatica, UFPE, Cidade Universitaria, Recife, PE, Brasil;Centro de Informatica, UFPE, Cidade Universitaria, Recife, PE, Brasil;Centro de Informatica, UFPE, Cidade Universitaria, Recife, PE, Brasil
Venue:
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Year:
2006

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Abstract

A partitioning cluster method for mixed feature-type symbolic data is presented. This method needs a previous pre-processing step to transform Boolean symbolic data into modal symbolic data. The presented dynamic clustering algorithm has then as input a set of vectors of modal symbolic data (weight distributions) and furnishes a partition and a prototype to each class by optimizing an adequacy criterion based on a suitable squared Euclidean distance. To show the usefulness of this method, examples with synthetic symbolic data sets and applications with real symbolic data sets are considered.