计算机技术与发展Issue(2)：1-6,10,7.DOI:10.3969/j.jssn.1673-629X.2013.02.001
基于双向选择调整策略的半监督聚类算法
Semi-supervised Clustering Algorithm Based on Double Adjustable Strategy
摘要
Abstract
Usually,semi-supervised clustering algorithms utilize a small amount of labeled data to improve cluster parameters which guide the clustering of unlabeled data. However,the existing semi-supervised clustering algorithms (such as cluster centroid) ignore the labeled data could directly affect the clustering of unlabeled data. It proposes a double adjustment strategy which adjusts unlabeled data clustering with the labeled information,after the data is clustered according to the cluster parameters. Thus,the proposed method improves the cluste-ring accuracy. The adjustment extension is changed dynamically by the local density around the labeled data. And a novel similarity meas-ure is proposed to improve the accuracy of the adjusted unlabeled data. It modifies two algorithms,based on multinomial model semi-su-pervised clustering algorithm and semi-supervised fuzzy clustering algorithm,with the double adjustment method. Experimental results show that the method could improve the accuracy of semi-supervised clustering.关键词
半监督聚类/未标注数据/标注数据/相似度/多项式模型/模糊聚类Key words
semi-supervised clustering/unlabeled data/labeled data/similarity/multinomial model/fuzzy clustering分类
信息技术与安全科学引用本文复制引用
刘明,宣照国,吴江宁..基于双向选择调整策略的半监督聚类算法[J].计算机技术与发展,2013,(2):1-6,10,7.基金项目
国家自然科学基金重点项目(71031002) （71031002）
国家自然科学基金资助项目(70871016) （70871016）
