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» Mining frequent itemsets in time-varying data streams
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KDD
2004
ACM
148views Data Mining» more  KDD 2004»
15 years 10 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
91
Voted
DASFAA
2007
IEEE
234views Database» more  DASFAA 2007»
15 years 4 months ago
Estimating Missing Data in Data Streams
Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and trackin...
Nan Jiang, Le Gruenwald
ISCC
2002
IEEE
147views Communications» more  ISCC 2002»
15 years 2 months ago
A new method for finding generalized frequent itemsets in generalized association rule mining
Generalized association rule mining is an extension of traditional association rule mining to discover more informative rules, given a taxonomy. In this paper, we describe a forma...
Kritsada Sriphaew, Thanaruk Theeramunkong
CIKM
2009
Springer
15 years 1 months ago
Efficient itemset generator discovery over a stream sliding window
Mining generator patterns has raised great research interest in recent years. The main purpose of mining itemset generators is that they can form equivalence classes together with...
Chuancong Gao, Jianyong Wang
RCIS
2010
14 years 8 months ago
A Tree-based Approach for Efficiently Mining Approximate Frequent Itemsets
—The strategies for mining frequent itemsets, which is the essential part of discovering association rules, have been widely studied over the last decade. In real-world datasets,...
Jia-Ling Koh, Yi-Lang Tu