A Kernel K-means Clustering Method for Symbolic Interval Data

Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. In this paper we present is an extension of kernel k-means clustering algorithm for symbolic interval data. To evaluate this method, experiments with synthetic and real interval data sets were performed and we have been compared our method with a dynamic clustering algorithm with adaptive distance. The evaluation is based on an external cluster validity index (corrected Rand index) and the overall error rate of classification (OERC). These experiments showed the usefulness of the proposed method and the results indicate that kernel clustering algorithm gives markedly better performance on data sets considered.