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» Phenomenal Data Mining: From Data to Phenomena
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KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 1 months ago
Locality sensitive hash functions based on concomitant rank order statistics
: Locality Sensitive Hash functions are invaluable tools for approximate near neighbor problems in high dimensional spaces. In this work, we are focused on LSH schemes where the si...
Kave Eshghi, Shyamsundar Rajaram
KDD
2008
ACM
146views Data Mining» more  KDD 2008»
16 years 1 months ago
Spotting out emerging artists using geo-aware analysis of P2P query strings
Record label companies would like to identify potential artists as early as possible in their careers, before other companies approach the artists with competing contracts. The va...
Noam Koenigstein, Yuval Shavitt, Tomer Tankel
KDD
2007
ACM
136views Data Mining» more  KDD 2007»
16 years 1 months ago
Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases
We now have incrementally-grown databases of text documents ranging back for over a decade in areas ranging from personal email, to news-articles and conference proceedings. While...
Benyah Shaparenko, Thorsten Joachims
WSDM
2010
ACM
322views Data Mining» more  WSDM 2010»
15 years 11 months ago
Inferring Search Behaviors Using Partially Observable Markov (POM) Model
This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant o...
Kuansan Wang, Nikolas Gloy, Xiaolong Li
ICAIL
2009
ACM
15 years 8 months ago
Integrating induction and deduction for finding evidence of discrimination
Automatic Decision Support Systems (DSS) are widely adopted for screening purposes in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and o...
Dino Pedreschi, Salvatore Ruggieri, Franco Turini