Description: 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.
- [Dominantset] - A relatively new clustering algorithm Do
- [Aterrainparabolicequatposphere] - A terrain parabolic equation model for p
- [apcluster] - Affinity Propagation Clustering dissemin
- [ap_semisupervised] - AP algorithm, add the supervision strate
- [AP_cluster] - AP Cluster
- [AP] - A new clustering algorithm is provided,
- [AP] - AP algorithm of some explanation, for th
- [AP] - Neighbor propagation algorithm, referred
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基于近邻传播算法的半监督聚类.pdf
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