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基于近邻传播算法的半监督聚类
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Supported by the National Natural Science Foundation of China under Grant No.60875031 (国家自然科学基金); the National Basic Research Program of China under Grant No.2007CB311002 (国家重点基础研究发展计划(973)); the Program for New Century Excellent Talents in University of China under Grant No.NECT-06-0078 (新世纪优秀人才支持计划); the Research Fund for the Doctoral Program of Higher Education of the Ministry of Education of China under Grant No.20050004008 (教育部高等学校博士学科点专项科研基金); the Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China under Grant No.101068 (霍英东教育基金会高等院校青年教师基金)


Semi-Supervised Clustering Based on Affinity Propagation Algorithm
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    摘要:

    提出了一种基于近邻传播(affinity propagation,简称AP)算法的半监督聚类方法.AP是在数据点的相似度矩阵的基础上进行聚类.对于规模很大的数据集,AP算法是一种快速、有效的聚类方法,这是其他传统的聚类算法所不能及的,比如:K中心聚类算法.但是,对于一些聚类结构比较复杂的数据集,AP算法往往不能得到很好的聚类结果.使用已知的标签数据或者成对点约束对数据形成的相似度矩阵进行调整,进而达到提高AP算法的聚类性能.实验结果表明,该方法不仅提高了AP对复杂数据的聚类结果,而且在约束对数量较多时,该方法要优于相关比对算法.

    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.

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肖 宇,于 剑.基于近邻传播算法的半监督聚类.软件学报,2008,19(11):2803-2813

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  • 收稿日期:2008-03-01
  • 最后修改日期:2008-08-26
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