Iterative Learning Assignment Order for Constrained K-means Algorithm
面向限制K-means算法的迭代学习分配次序策略

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(UALA) 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.