Abstract
Similarity is the core problem of clustering. Clustering algorithms that are based on a certain, fixed type of similarity are not sufficient to explore complicated structures. In this paper, a constructing method for multiple similarity is proposed to deal with complicated structures of data sets. Multiple similarity derives from the local modification of the initial similarity, based on the feedback information of elementary clusters. Combined with the proposed algorithm, the repeated modifications of local similarity measurement generate a hierarchical clustering result. Some synthetic and real data sets are employed to exhibit the superiority of the new clustering algorithm.
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