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KDD
2009
ACM
239views Data Mining» more  KDD 2009»
16 years 7 months ago
Tell me something I don't know: randomization strategies for iterative data mining
There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however...
Heikki Mannila, Kai Puolamäki, Markus Ojala, ...
KDD
2004
ACM
154views Data Mining» more  KDD 2004»
16 years 6 months ago
Diagnosing extrapolation: tree-based density estimation
There has historically been very little concern with extrapolation in Machine Learning, yet extrapolation can be critical to diagnose. Predictor functions are almost always learne...
Giles Hooker
KDD
2001
ACM
118views Data Mining» more  KDD 2001»
16 years 6 months ago
Infominer: mining surprising periodic patterns
In this paper, we focus on mining surprising periodic patterns in a sequence of events. In many applications, e.g., computational biology, an infrequent pattern is still considere...
Jiong Yang, Wei Wang 0010, Philip S. Yu
STOC
2005
ACM
129views Algorithms» more  STOC 2005»
16 years 6 months ago
Learning with attribute costs
We study an extension of the "standard" learning models to settings where observing the value of an attribute has an associated cost (which might be different for differ...
Haim Kaplan, Eyal Kushilevitz, Yishay Mansour
SIGMOD
2006
ACM
111views Database» more  SIGMOD 2006»
16 years 6 months ago
Reconciling while tolerating disagreement in collaborative data sharing
In many data sharing settings, such as within the biological and biomedical communities, global data consistency is not always attainable: different sites' data may be dirty,...
Nicholas E. Taylor, Zachary G. Ives