Properties Of Potential Function- Based Clustering Algorithms
The clustering algorithms based on potential functions are capable of clustering a set of data, making no implicit assumptions on the cluster shapes and without knowing in advance the number of clusters. They are similarity-based type clustering algorithms and do not use any prototype vectors of the clusters. In this paper, some properties of these algorithms are studied: points arrangement tendency, constant potential surface, cluster shapes and robustness to noise.
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