Improved adaptive affinity propagation clustering based on semi-supervised learning
一种结合半监督的改进自适应亲和传播聚类

The existing adaptive affinity propagation clustering has some shortcoming,such as long runtime and low accuracy. This paper proposed an improved adaptive affinity propagation clustering based on semi-supervised learning (SAAP).It first updated the similarity matrix by semi-supervised learning, then scaned adaptively the effective clustersing space based on Affinity propagation clustering by the dichotomy judge,and finally determined the best clustering by the weighted evaluation function.The experiments show, SAAP algorithm can more quickly scan effective clustering space, and can be smaller misclassification rate and a higher effectiveness evaluation index.