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» Boosting margin based distance functions for clustering
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JCP
2008
162views more  JCP 2008»
13 years 8 months ago
Shape Recognition by Clustering and Matching of Skeletons
We perform the task of shape recognition using a skeleton based method. Skeleton of the shape is considered as a free tree and is represented by a connectivity graph. Geometric fea...
Hamidreza Zaboli, Mohammad Rahmati, Abdolreza Mirz...
PAKDD
2010
ACM
198views Data Mining» more  PAKDD 2010»
14 years 1 months ago
A Better Strategy of Discovering Link-Pattern Based Communities by Classical Clustering Methods
Abstract. The definition of a community in social networks varies with applications. To generalize different types of communities, the concept of linkpattern based community was pr...
Chen-Yi Lin, Jia-Ling Koh, Arbee L. P. Chen
SSDBM
2005
IEEE
218views Database» more  SSDBM 2005»
14 years 2 months ago
The "Best K" for Entropy-based Categorical Data Clustering
With the growing demand on cluster analysis for categorical data, a handful of categorical clustering algorithms have been developed. Surprisingly, to our knowledge, none has sati...
Keke Chen, Ling Liu
ICML
2003
IEEE
14 years 9 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
SSDBM
2005
IEEE
159views Database» more  SSDBM 2005»
14 years 2 months ago
Clustering Moving Objects via Medoid Clusterings
Modern geographic information systems do not only have to handle static information but also dynamically moving objects. Clustering algorithms for these moving objects provide new...
Hans-Peter Kriegel, Martin Pfeifle