Clustering the Linearly Inseparable Clusters
In this paper a new method for clustering is introduced that works fine for linearly inseparable clusters. This algorithm uses the of patterns’ concentration specification. We consider each pattern as a heat source to determine concentration by heat effect. Because of high concentration in clusters, heat in the clusters is higher than out of them, and this fact is used to determine and bound the clusters. CLIC algorithm can be implemented for any linearly inseparable cluster. The classification rate of this algorithm is higher than previously known methods (k-means, fuzzy c-means, etc). Finally, we cluster real iris data by using CLIC algorithm.
KeywordsArbitrary Point Classification Rate Heat Function Iris Data Main Maximum
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