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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 7 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
2004
ACM
135views Data Mining» more  KDD 2004»
16 years 7 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker
SIGMOD
2004
ACM
184views Database» more  SIGMOD 2004»
16 years 7 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...
VLDB
2007
ACM
137views Database» more  VLDB 2007»
16 years 27 days ago
Detecting Attribute Dependencies from Query Feedback
Real-world datasets exhibit a complex dependency structure among the data attributes. Learning this structure is a key task in automatic statistics configuration for query optimi...
Peter J. Haas, Fabian Hueske, Volker Markl