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KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 6 months ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
BMCBI
2010
153views more  BMCBI 2010»
15 years 6 months ago
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or su...
Eva Freyhult, Mattias Landfors, Jenny Önskog,...
ICDM
2003
IEEE
71views Data Mining» more  ICDM 2003»
15 years 11 months ago
Tree-structured Partitioning Based on Splitting Histograms of Distances
We propose a novel clustering algorithm that is similar in spirit to classification trees. The data is recursively split using a criterion that applies a discrete curve evolution...
Longin Jan Latecki, Rajagopal Venugopal, Marc Sobe...
KDD
2009
ACM
140views Data Mining» more  KDD 2009»
16 years 6 months ago
Improving clustering stability with combinatorial MRFs
: ? Improving Clustering Stability with Combinatorial MRFs Bekkerman, Ron; Scholz, Martin; Viswanathan, Krishnamurthy HP Laboratories HPL-2009-46 Clustering stability, combinatoria...
Ron Bekkerman, Martin Scholz, Krishnamurthy Viswan...
ICDM
2007
IEEE
137views Data Mining» more  ICDM 2007»
16 years 17 days ago
Locally Constrained Support Vector Clustering
Support vector clustering transforms the data into a high dimensional feature space, where a decision function is computed. In the original space, the function outlines the bounda...
Dragomir Yankov, Eamonn J. Keogh, Kin Fai Kan