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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 4 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
KDD
2007
ACM
201views Data Mining» more  KDD 2007»
16 years 4 months ago
Structural and temporal analysis of the blogosphere through community factorization
The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel tech...
Yun Chi, Shenghuo Zhu, Xiaodan Song, Jun'ichi Tate...
KDD
2007
ACM
152views Data Mining» more  KDD 2007»
16 years 4 months ago
Relational data pre-processing techniques for improved securities fraud detection
Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarel...
Andrew Fast, Lisa Friedland, Marc Maier, Brian Tay...
KDD
2007
ACM
184views Data Mining» more  KDD 2007»
16 years 4 months ago
Correlation search in graph databases
Correlation mining has gained great success in many application domains for its ability to capture the underlying dependency between objects. However, the research of correlation ...
Yiping Ke, James Cheng, Wilfred Ng
KDD
2006
ACM
134views Data Mining» more  KDD 2006»
16 years 4 months ago
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal
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