K-means Clustering for Symbolic Interval Data Based on Aggregated Kernel Functions

In this paper we propose is an extension of kernel k-means clustering algorithm for symbolic interval data with aggregated kernel functions. To evaluate this method, experiments with synthetic interval data set was performed and we have been compared our method with a dynamic clustering algorithm with single adaptive distance. The evaluation is based on an external cluster validity index (corrected Rand index) and the overall error rate of classification (OERC). This experiment showed the usefulness of the proposed method and the results indicate that aggregated kernel clustering algorithm gives markedly better performance on data sets considered.