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HCS clustering algorithm
The ( HCS (Highly Connected Subgraphs) clustering algorithm ) (also known as the HCS algorithm , and other names such as Highly Connected Clusters/Components/Kernels) is an algorithm based on graph connectivity for Cluster analysis, by first representing the similarity data in a similarity graph, and afterwards finding all the highly connected subgraphs as clusters. The algorithm does not make any prior assumptions on the number of the clusters. This algorithm was published by Erez Hartuv (erez dot hartuv at gmail dot com) and Ron Shamir in 1998.
The HCS algorithm gives clustering solution, which is inherently meaningful in the application domain, since each solution cluster must have diameter 2 while a union of two solution clusters will have diameter 3.
== Similarity Modeling and Preprocessing ==
The goal of cluster analysis is to group elements into disjoint subsets, or clusters, based on similarity between elements, so that elements in the same cluster are highly similar to each other (homogeneity), while elements from different clusters have low similarity to each other (separation). Similarity graph is one of the models to represent the similarity between elements, and in turn facilitate generating of clusters. To construct a similarity graph from similarity data, represent elements as vertices, and elicit edges between vertices when the similarity value between them is above some threshold.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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