基于成对约束的交叉熵半监督聚类算法*

极大熵聚类(MEC)目标函数中缺乏成对约束的有效信息表达，在拥有少量成对约束的情况下，可能导致有效监督信息的浪费.在MEC的基础上，文中提出基于成对约束的交叉熵半监督聚类算法.利用样本交叉熵表达成对约束信息，并作为惩罚项引入至MEC的目标函数中，通过拉格朗日最优化处理目标函数，得出聚类中心与隶属度的迭代公式.实验表明，文中算法能有效利用少量的成对约束监督信息提高聚类性能，在实际数据应用中性能较好

Cross-Entropy Semi-supervised Clustering Based on Pairwise Constraints

The objective function used in the classical maximum entropy clustering(MEC) lacks the information expression on pairwise constraints. Therefore, the effective supervision information is wasted when a small amount of pairwise constraints are known. In this paper, an algorithm of cross-entropy semi-supervised clustering(CE-sSC) based on pairwise constrains on the basis of MEC algorithm is proposed. The sample cross-entropy is utilized to describe the pairwise constraints information and introduced to the objective function of MEC as a penalty term. With Lagrange optimization procedure, the objective function can be resolved into the cluster center and the membership update equations. Experimental results indicate the proposed method effectively improves the clustering performance by using a small amount of pairwise constraints and works well on actual datasets.