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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.
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.
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基于近邻传播算法的半监督聚类.pdf