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» Clustering Large Datasets in Arbitrary Metric Spaces
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BMCBI
2008
117views more  BMCBI 2008»
13 years 7 months ago
New resampling method for evaluating stability of clusters
Background: Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in cluste...
Irina Gana Dresen, Tanja Boes, Johannes Hüsin...
PVLDB
2010
86views more  PVLDB 2010»
13 years 5 months ago
Swarm: Mining Relaxed Temporal Moving Object Clusters
Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existin...
Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays
MM
2009
ACM
252views Multimedia» more  MM 2009»
14 years 1 months ago
Localizing volumetric motion for action recognition in realistic videos
This paper presents a novel motion localization approach for recognizing actions and events in real videos. Examples include StandUp and Kiss in Hollywood movies. The challenge ca...
Xiao Wu, Chong-Wah Ngo, Jintao Li, Yongdong Zhang
SDM
2009
SIAM
205views Data Mining» more  SDM 2009»
14 years 4 months ago
Identifying Information-Rich Subspace Trends in High-Dimensional Data.
Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in m...
Chandan K. Reddy, Snehal Pokharkar
WEA
2005
Springer
176views Algorithms» more  WEA 2005»
14 years 26 days ago
High-Performance Algorithm Engineering for Large-Scale Graph Problems and Computational Biology
Abstract. Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on regular applications with high degre...
David A. Bader