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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...
Clustering is the problem of finding relations in a data set in an supervised manner. These relations can be extracted using the density of a data set, where density of a data point is defined as the number of data points around it. To find the number of data points around another point, region queries are adopted. Region queries are the most expensive construct in density based algorithm, so it should...
Given a set of clusters, we consider an optimization problem which seeks a subset of clusters that maximizes the microaverage F-measure. This optimal value can be used as an evaluation measure of the goodness of clustering. For arbitrarily overlapping clusters, finding the optimal value is NP-hard. We claim that a greedy approximation algorithm yields the global optimal solution for clusters that...
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