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IPMI
2007
Springer
14 years 8 months ago
Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts
A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires p...
Mahnaz Maddah, William M. Wells III, Simon K. Warf...
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 7 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney
CVPR
2011
IEEE
12 years 11 months ago
Max-margin Clustering: Detecting Margins from Projections of Points on Lines
Given a unlabelled set of points X ∈ RN belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We...
Raghuraman Gopalan, Jagan Sankaranarayanan
ICCV
2009
IEEE
1821views Computer Vision» more  ICCV 2009»
15 years 5 days ago
Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering
We present an efficient method for feature correspondence and object-based image matching, which exploits both photometric similarity and pairwise geometric consistency from local ...
Minsu Cho (Seoul National University), Jungmin Lee...
SDM
2007
SIAM
176views Data Mining» more  SDM 2007»
13 years 8 months ago
Adaptive Concept Learning through Clustering and Aggregation of Relational Data
We introduce a new approach for Clustering and Aggregating Relational Data (CARD). We assume that data is available in a relational form, where we only have information about the ...
Hichem Frigui, Cheul Hwang