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» Approximate data mining in very large relational data
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KDD
2005
ACM
107views Data Mining» more  KDD 2005»
15 years 10 months ago
Predicting the product purchase patterns of corporate customers
This paper describes TIPPPS (Time Interleaved Product Purchase Prediction System), which analyses billing data of corporate customers in a large telecommunications company in orde...
Bhavani Raskutti, Alan Herschtal
SIGMOD
2004
ACM
184views Database» more  SIGMOD 2004»
16 years 4 months ago
CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies
The rich dependency structure found in the columns of real-world relational databases can be exploited to great advantage, but can also cause query optimizers--which usually assum...
Ihab F. Ilyas, Volker Markl, Peter J. Haas, Paul B...
ICDE
2010
IEEE
227views Database» more  ICDE 2010»
15 years 11 months ago
Incorporating partitioning and parallel plans into the SCOPE optimizer
— Massive data analysis on large clusters presents new opportunities and challenges for query optimization. Data partitioning is crucial to performance in this environment. Howev...
Jingren Zhou, Per-Åke Larson, Ronnie Chaiken
PKDD
2007
Springer
131views Data Mining» more  PKDD 2007»
15 years 10 months ago
Expectation Propagation for Rating Players in Sports Competitions
Abstract. Rating players in sports competitions based on game results is one example of paired comparison data analysis. Since an exact Bayesian treatment is intractable, several t...
Adriana Birlutiu, Tom Heskes
SDM
2010
SIAM
226views Data Mining» more  SDM 2010»
15 years 5 months ago
Two-View Transductive Support Vector Machines
Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam...
Guangxia Li, Steven C. H. Hoi, Kuiyu Chang