Abstract: Abstract：In this paper, the maximum-entropy approach is introduced to semi-supervised clustering, and a novel semi-supervised clustering algorithm AMESC based on auxiliary space, maximum entropy and simulated annealing is proposed. This algorithm realizes the efficient clustering by minimizing the cost function iteratively. Our experimental results demonstrate its validity and superiority.

摘要： 摘 要 ：将极大熵原理引入半监督聚类方法中，提出基于辅助空间与极大熵的半监督聚类算法AMESC，针对该算法中的代价函数进行迭代优化，实现聚类。AMESC的优势在于它依据模拟退火过程,使算法避开局部极小而得到全局极小，提高算法性能。通过实验证实了AMESC的有效性和优越性。