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KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 8 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
INFORMATICALT
2008
124views more  INFORMATICALT 2008»
13 years 7 months ago
Hierarchical Adaptive Clustering
This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We prop...
Gabriela Serban, Alina Campan
SIGMOD
2004
ACM
154views Database» more  SIGMOD 2004»
14 years 7 months ago
Computing Clusters of Correlation Connected Objects
The detection of correlations between different features in a set of feature vectors is a very important data mining task because correlation indicates a dependency between the fe...
Christian Böhm, Karin Kailing, Peer Krög...
KDD
2005
ACM
157views Data Mining» more  KDD 2005»
14 years 8 months ago
A fast kernel-based multilevel algorithm for graph clustering
Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
EDBT
2006
ACM
129views Database» more  EDBT 2006»
14 years 7 months ago
Efficient Quantile Retrieval on Multi-dimensional Data
Given a set of N multi-dimensional points, we study the computation of -quantiles according to a ranking function F, which is provided by the user at runtime. Specifically, F compu...
Man Lung Yiu, Nikos Mamoulis, Yufei Tao