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This paper presents a hybrid clustering algorithm based on density and ant colony algorithm, that to determine the initial cluster centers according to cluster objects distribution density method, and then use the swarm intelligence and randomness of ant colony algorithm to find that arbitrary shape of clusters, to avoid falling into local convergence, to get a relatively stable global optimal solution...
With the purpose to reduce the surplus information on decision table and extract the determinative rules, an autonomous clustering algorithm based on graded datum subtraction (ACGDS) is proposed to reduce the data area and an attribute reduction algorithm based on ant colony optimization (ARACO) is presented to reduce the surplus attributes. ACGDS uses the quick sort method and subtraction to every...
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