Robust clustering algorithm for the symbolic interval-values data with outliers

In this study, the novel robust clustering algorithm, robust interval competitive agglomeration (RICA) clustering algorithm, is proposed to overcome the problems of the outliers and the numbers of cluster in the competitive agglomeration clustering algorithm for the symbolic interval-values data. The Euclidean distance measure is considered in the proposed RICA clustering algorithm. Moreover, the RICA clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Additionally, the RICA clustering algorithm is also converges to the same optimal partition regardless of its initialization. Experimentally results show the merits and usefulness of the RICA clustering algorithm for the symbolic interval-values data.