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SDM
2008
SIAM
168views Data Mining» more  SDM 2008»
15 years 8 months ago
Semi-Supervised Clustering via Matrix Factorization
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. U...
Fei Wang, Tao Li, Changshui Zhang
ICML
2005
IEEE
16 years 7 months ago
Clustering through ranking on manifolds
Clustering aims to find useful hidden structures in data. In this paper we present a new clustering algorithm that builds upon the consistency method (Zhou, et.al., 2003), a semi-...
Markus Breitenbach, Gregory Z. Grudic
ECML
2007
Springer
16 years 21 days ago
Spectral Clustering and Embedding with Hidden Markov Models
Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering ...
Tony Jebara, Yingbo Song, Kapil Thadani
KDD
2004
ACM
157views Data Mining» more  KDD 2004»
15 years 12 months ago
On detecting space-time clusters
Detection of space-time clusters is an important function in various domains (e.g., epidemiology and public health). The pioneering work on the spatial scan statistic is often use...
Vijay S. Iyengar
BMVC
2002
15 years 9 months ago
Alignment using Spectral Clusters
This paper describes a hierarchical spectral method for the correspondence matching of point-sets. Conventional spectral methods for correspondence matching are notoriously suscep...
Marco Carcassoni, Edwin R. Hancock