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SSPR
2004
Springer
14 years 27 days ago
Clustering with Soft and Group Constraints
Several clustering algorithms equipped with pairwise hard constraints between data points are known to improve the accuracy of clustering solutions. We develop a new clustering alg...
Martin H. C. Law, Alexander P. Topchy, Anil K. Jai...
MLMTA
2007
13 years 9 months ago
Consensus Based Ensembles of Soft Clusterings
— Cluster Ensembles is a framework for combining multiple partitionings obtained from separate clustering runs into a final consensus clustering. This framework has attracted mu...
Kunal Punera, Joydeep Ghosh
CVIU
2007
112views more  CVIU 2007»
13 years 7 months ago
Pedestrian detection and tracking in infrared imagery using shape and appearance
In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appearance cues. A layered representation is first intr...
Congxia Dai, Yunfei Zheng, Xin Li
SDM
2004
SIAM
212views Data Mining» more  SDM 2004»
13 years 9 months ago
Clustering with Bregman Divergences
A wide variety of distortion functions, such as squared Euclidean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been used for clustering. In th...
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhil...
CVPR
2008
IEEE
14 years 9 months ago
Context-aware clustering
Most existing methods of semi-supervised clustering introduce supervision from outside, e.g., manually label some data samples or introduce constrains into clustering results. Thi...
Junsong Yuan, Ying Wu