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» Clustering Rules Using Empirical Similarity of Support Sets
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DIS
2001
Springer
13 years 10 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 10 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 6 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 6 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