一本将基于近邻传播算法的半监督聚类的算方法书.对于聚类研究的很有帮助-Abstract:　　A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this

paper. AP takes as input measures of similarity between pairs of data points. AP is an efficient and fast clustering

algorithm for large dataset compared with the existing clustering algorithms, such as K-center clustering. But for the

datasets with complex cluster structures, it cannot produce good clustering results. It can improve the clustering

performance of AP by using the priori known labeled data or pairwise constraints to adjust the similarity matrix.

Experimental results show that such method indeed reaches its goal for complex datasets, and this method

outperforms the comparative methods when there are a large number of pairwise constraints.