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Author Xu, Qianjun ♦ Desjardins, Marie ♦ Wagstaff, Kiri L.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Pairwise Constraint ♦ Access Method ♦ Active Selection ♦ Spectral Eigenvectors ♦ Real Data Set ♦ Cluster Description ♦ Active Constrained Clustering ♦ Data Item ♦ Spectral Decomposition ♦ Spectral Clustering ♦ Theoretical Property ♦ Similarity Matrix ♦ Empirical Result
Description This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (AC-CESS) derived from a similarity matrix. The ACCESS method uses an analysis based on the theoretical properties of spectral decomposition to identify data items that are likely to be located on the boundaries of clusters, and for which providing constraints can resolve ambiguity in the cluster descriptions. Empirical results on three synthetic and five real data sets show that ACCESS significantly outperforms constrained spectral clustering using randomly selected constraints.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2005-01-01
Publisher Institution IN: PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON DISCOVERY SCIENCE