面向限制K-means算法的迭代学习分配次序策略

摘要

结合关联限制K-means算法能有效地提高聚类结果,但对数据对象分配次序却非常敏感.为获得一个好的分配次序,提出了一种基于分配次序聚类不稳定性的迭代学习算法.根据Cop-Kmeans算法的稳定性特点,采用迭代思想,逐步确定数据对象的稳定性,进而确定分配次序.实验结果表明,基于分配次序聚类不稳定性迭代学习算法有效地提高了Cop-Kmeans算法的准确率.%Constrained K-means algorithm often improves clustering accuracy, but sensitive to the assignment order of instances. A clustering uncertainty based assignment order Iterative Learning Algorithm(UAILA) was proposed to gain a good assignment order. The instances stability was gradually confirmed by iterative thought according to the characteristics of Cop-Kmeans algorithm stability, and then assignment order was confirmed. The experiment demonstrates that the algorithm effectively improves the accuracy of Cop-Kmeans algorithm.