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KDD
2008
ACM
140views Data Mining» more  KDD 2008»
16 years 6 months ago
Semi-supervised approach to rapid and reliable labeling of large data sets
Supervised classification methods have been shown to be very effective for a large number of applications. They require a training data set whose instances are labeled to indicate...
György J. Simon, Vipin Kumar, Zhi-Li Zhang
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
16 years 6 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
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...
KDD
2004
ACM
108views Data Mining» more  KDD 2004»
16 years 6 months ago
Sleeved coclustering
A coCluster of a m?n matrix X is a submatrix determined by a subset of the rows and a subset of the columns. The problem of finding coClusters with specific properties is of inter...
Avraham A. Melkman, Eran Shaham
KDD
2004
ACM
126views Data Mining» more  KDD 2004»
16 years 6 months ago
Dense itemsets
Frequent itemset mining has been the subject of a lot of work in data mining research ever since association rules were introduced. In this paper we address a problem with frequen...
Heikki Mannila, Jouni K. Seppänen
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