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» Clustering Rules Using Empirical Similarity of Support Sets
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DIS
2001
Springer
13 years 9 months ago
Clustering Rules Using Empirical Similarity of Support Sets
Shreevardhan Lele, Bruce L. Golden, Kimberly Ozga,...
ICML
2005
IEEE
14 years 6 months ago
Supervised clustering with support vector machines
Supervised clustering is the problem of training a clustering algorithm to produce desirable clusterings: given sets of items and complete clusterings over these sets, we learn ho...
Thomas Finley, Thorsten Joachims
DMKD
1997
ACM
198views Data Mining» more  DMKD 1997»
13 years 9 months ago
Clustering Based On Association Rule Hypergraphs
Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. These d...
Eui-Hong Han, George Karypis, Vipin Kumar, Bamshad...
KDD
2005
ACM
139views Data Mining» more  KDD 2005»
14 years 5 months ago
Reasoning about sets using redescription mining
Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions. It can be viewed as a generalization of associa...
Mohammed Javeed Zaki, Naren Ramakrishnan
JCST
2008
138views more  JCST 2008»
13 years 5 months ago
Predicting Chinese Abbreviations from Definitions: An Empirical Learning Approach Using Support Vector Regression
In Chinese, phrases and named entities play a central role in information retrieval. Abbreviations, however, make keyword-based approaches less effective. This paper presents an em...
Xu Sun, Houfeng Wang, Bo Wang 0003