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» Theoretical Frameworks for Data Mining
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PKDD
1999
Springer
106views Data Mining» more  PKDD 1999»
14 years 1 months ago
Heuristic Measures of Interestingness
When mining a large database, the number of patterns discovered can easily exceed the capabilities of a human user to identify interesting results. To address this problem, variou...
Robert J. Hilderman, Howard J. Hamilton
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
14 years 9 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
KDD
2003
ACM
124views Data Mining» more  KDD 2003»
14 years 9 months ago
Information-theoretic co-clustering
Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingenc...
Inderjit S. Dhillon, Subramanyam Mallela, Dharmend...
PKDD
1999
Springer
130views Data Mining» more  PKDD 1999»
14 years 1 months ago
OPTICS-OF: Identifying Local Outliers
: For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commer...
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. ...
ICDE
2007
IEEE
165views Database» more  ICDE 2007»
14 years 10 months ago
On Randomization, Public Information and the Curse of Dimensionality
A key method for privacy preserving data mining is that of randomization. Unlike k-anonymity, this technique does not include public information in the underlying assumptions. In ...
Charu C. Aggarwal