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Improved clustering algorithm with adaptive opposition-based learning

In recent years, clustering has become a hotspot in the field of data mining, as one of the key technologies of getting data distribution and observing the characteristics of class. However, some clustering algorithms depend on the selection of initial clustering centers, and the clustering results easily fall into local optimal. To solve the above problem, the paper integrates differential evolution algorithm and adaptive opposition-based learning. The algorithm makes use of reverse factor to guide algorithm search space approaching to the global optimal solution in each generation. In this paper, the improved algorithm is combined with classical K-means algorithm. According to the result of the three sets of data from UCI data verification, it demonstrates that the improved clustering algorithm can not only cluster better and converge faster, but also effectively suppress the occurrence of prematurity.