Abstract
A new clustering algorithm, called reciprocating link hierarchical clustering, is proposed which considers the neighborhood of the points in the data set in term of their reciprocating affinity, while accommodating the agglomerative hierarchical clustering paradigm. In comparison to six conventional clustering methods, the proposed method has been shown to achieve better results with cases of clusters of different sizes and varying densities. It successfully replicates the results of the mutual k-nearest neighbor method, and extends the capability to agglomerative hierarchical clustering.
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