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Combining with the conception of minimum spanning tree in graph theory and with entropy in information theory, a new algorithm is proposed for clustering. An objective function of the weighted entropy based on intra-variance in cluster and variance between clusters is built. The cluster result for the data set is derived from the maximum objective function. This algorithm doesn't need the prior knowledge...
Combining with the graph theory clustering methods, an entropy-objective function algorithm was proposed for clustering. The edge which connects the vertices in non-orientation graph was redefined according to the distribution and distance of the data set. The objective function of the weighted entropy based on intra-variance in cluster and variance between clusters was built. The cluster result for...
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