A partitioning method for symbolic interval data based on kernelized metric

Authors:
Bruno Pimentel;Anderson Costa;Renata Souza
Affiliations:
Universidade Federal de Pernambuco, Recife, Brazil;Universidade Federal de Pernambuco, Recife, Brazil;Universidade Federal de Pernambuco, Recife, Brazil
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

To solve the problem of situations with nonlinearly separable clusters, kernel clustering methods have been proposed. Symbolic Data Analysis (SDA) has emerged to deal with variables that can have intervals, histograms, and even functions as values, in order to consider the variability and/or uncertainty innate to the data. In this paper, we present a K-means clustering method based in kernelized squared L2 distance for symbolic interval-type data. Experiments with real and syntectic symbolic interval-type data sets are considered.