Semi-Supervised Clustering via Matrix Factorization

Abstract

Next chapter Full AccessProceedings Proceedings of the 2008 SIAM International Conference on Data Mining (SDM)Semi-Supervised Clustering via Matrix FactorizationFei Wang, Tao Li, and Changshui ZhangFei Wang, Tao Li, and Changshui Zhangpp.1 - 12Chapter DOI:https://doi.org/10.1137/1.9781611972788.1PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to cluster the data set under the guidance of some supervisory information. Usually those supervisory information takes the form of pairwise constraints that indicate the similarity/dissimilarity between the two points. In this paper, we propose a novel matrix factorization based approach for semi-supervised clustering. In addition, we extend our algorithm to co-cluster the data sets of different types with constraints. Finally the experiments on UCI data sets and real world Bulletin Board Systems (BBS) data sets show the superiority of our proposed method. Next chapter RelatedDetails Published:2008ISBN:978-0-89871-654-2eISBN:978-1-61197-278-8 https://doi.org/10.1137/1.9781611972788Book Series Name:ProceedingsBook Code:PR130Book Pages:1-869