期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

半监督聚类算法研究现状 被引量：4

Research on the Present Situation of Semi-Supervised Clustering Algorithm
在线阅读 下载PDF
导出
摘要 半监督聚类是近几年机器学习领域的一个新的研究方向,也是数据挖掘的一个重要分支,逐步成为许多领域的有用工具。对数据挖掘半监督聚类算法的研究现状及发展趋势进行了分析与概括,并比较分析几种典型半监督聚类算法的优点与局限性,以便于对半监督聚类算法作进一步的研究。 Semi-supervised clustering is a hot topic of machine learning in recent years, and is a important branch of data mining, is becoming a growingly useful tool in many applications. Summarizes the main ideal of existing semi-supervised clustering algorithms in data mining and points out the limitation of the algorithms, puts forwrad some research directions about semi-supervised clustering in future work.
作者 熊建斌 李振坤 刘怡俊
机构地区 广东工业大学计算机学院
出处 《现代计算机》 2009年第12期61-64,77,共5页 Modern Computer
关键词 半监督聚类 数据挖掘 判别法 Semi-Supervised Clustering Data Mining Similarity-Adapting Methods
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献7

二级参考文献71

  • 1RODuda PEHart著 李宏东 姚天翔译.模式分类[M].北京:机械工业出版社,2003-09..
  • 2Basu S, Banerjee A, Mooney RJ. A probabilistic framework for semi-supervised clustering. In: Boulicaut JF, Esposito F, Giannotti F, Pedreschi D, eds. Proc. of the 10th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2004.59-68.
  • 3Bilenko M, Basu S, Mooney RJ. Integrating constraints and metric learning in semi-supervised clustering. In: Brodley CE, ed. Proc. of the 21st Int'l Conf. on Machine Learning. New York: ACM Press, 2004. 81-88.
  • 4Tang W, Xiong H, Zhong S, Wu J. Enhancing semi-supervised clustering: a feature projection perspective. In: Berkhin P, Caruana R, Wu XD, eds. Proc. of the 13th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2007. 707-716.
  • 5Basu S, Banerjee A, Mooney RJ. Active semi-supervision for pairwise constrained clustering. In: Jonker W, Petkovic M, eds. Proc. of the SIAM Int'l Conf. on Data Mining. Cambridge: MIT Press, 2004. 333-344.
  • 6Yan B, Domeniconi C. An adaptive kernel method for semi-supervised clustering. In: Fiirnkranz J, Scheffer T, Spiliopoulou M, eds. Proc. of the 17th European Conf. on Machine Learning. Berlin: Sigma Press, 2006. 18-22.
  • 7Yeung DY, Chang H. Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints. Pattern Recognition, 2006,39(5):1007-1010.
  • 8Beyer K, Goldstein J, Ramakrishnan R, Shaft U. When is "Nearest Neighbors Meaningful"? In: Beeri C, Buneman P, eds. Proc. of the Int'l Conf. on Database Theory. New York: ACM Press, 1999.217-235.
  • 9Ding CH, Li T. Adaptive dimension reduction using discriminant analysis and K-means clustering. In: Ghahramani Z, ed. Proc. of the 19th Int'l Conf. on Machine Learning. New York: ACM Press, 2007.521-528.
  • 10Zhang DQ, Zhou ZH, Chen SC. Semi-Supervised dimensionality reduction. In: Mandoiu I, Zelikovsky A, eds. Proc. of the 7th SIAM Int'l Conf. on Data Mining. Cambridge: MIT Press, 2007. 629-634.

共引文献90

同被引文献29

引证文献4

二级引证文献27

现代计算机
2009年 第12期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部