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» Ontology-Enhanced Association Mining
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KDD
2006
ACM
130views Data Mining» more  KDD 2006»
14 years 9 months ago
Discovering significant rules
In many applications, association rules will only be interesting if they represent non-trivial correlations between all constituent items. Numerous techniques have been developed ...
Geoffrey I. Webb
KDD
2004
ACM
148views Data Mining» more  KDD 2004»
14 years 9 months ago
Interestingness of frequent itemsets using Bayesian networks as background knowledge
The paper presents a method for pruning frequent itemsets based on background knowledge represented by a Bayesian network. The interestingness of an itemset is defined as the abso...
Szymon Jaroszewicz, Dan A. Simovici
PKDD
2001
Springer
185views Data Mining» more  PKDD 2001»
14 years 1 months ago
Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB
Feature extraction and knowledge discovery from a large amount of image data such as remote sensing images have become highly required recent years. In this study, a framework for ...
Rie Honda, Osamu Konishi
TCS
2008
13 years 8 months ago
Itemset frequency satisfiability: Complexity and axiomatization
Computing frequent itemsets is one of the most prominent problems in data mining. We study the following related problem, called FREQSAT, in depth: given some itemset-interval pai...
Toon Calders
CIKM
2010
Springer
13 years 7 months ago
Generating advertising keywords from video content
With the proliferation of online distribution methods for videos, content owners require easier and more effective methods for monetization through advertising. Matching advertis...
Michael J. Welch, Junghoo Cho, Walter Chang