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» Phenomenal Data Mining: From Data to Phenomena
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KDD
2008
ACM
199views Data Mining» more  KDD 2008»
16 years 2 months ago
Building semantic kernels for text classification using wikipedia
Document classification presents difficult challenges due to the sparsity and the high dimensionality of text data, and to the complex semantics of the natural language. The tradi...
Pu Wang, Carlotta Domeniconi
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
16 years 2 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 2 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
ICDM
2009
IEEE
211views Data Mining» more  ICDM 2009»
15 years 8 months ago
Discovering Organizational Structure in Dynamic Social Network
—Applying the concept of organizational structure to social network analysis may well represent the power of members and the scope of their power in a social network. In this pap...
Jiangtao Qiu, Zhangxi Lin, Changjie Tang, Shaojie ...
PKDD
2009
Springer
120views Data Mining» more  PKDD 2009»
15 years 8 months ago
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn fea...
Shuang-Hong Yang, Hongyuan Zha, Shaohua Kevin Zhou...