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» Mining High Utility Itemsets in Big Data
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KDD
2001
ACM
196views Data Mining» more  KDD 2001»
15 years 10 months ago
Efficient discovery of error-tolerant frequent itemsets in high dimensions
We present a generalization of frequent itemsets allowing the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifie...
Cheng Yang, Usama M. Fayyad, Paul S. Bradley
KAIS
2008
150views more  KAIS 2008»
14 years 10 months ago
A survey on algorithms for mining frequent itemsets over data streams
The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of freq...
James Cheng, Yiping Ke, Wilfred Ng
DAWAK
2004
Springer
15 years 3 months ago
Algorithms for Discovery of Frequent Superset, Rather than Frequent Subset
Abstract. In this paper, we propose a novel mining task: mining frequent superset from the database of itemsets that is useful in bioinformatics, e-learning systems, jobshop schedu...
Zhung-Xun Liao, Man-Kwan Shan
KDD
2009
ACM
168views Data Mining» more  KDD 2009»
15 years 5 months ago
Cartesian contour: a concise representation for a collection of frequent sets
In this paper, we consider a novel scheme referred to as Cartesian contour to concisely represent the collection of frequent itemsets. Different from the existing works, this sche...
Ruoming Jin, Yang Xiang, Lin Liu
EMS
2008
IEEE
15 years 4 months ago
A Weighted Utility Framework for Mining Association Rules
Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by assuming that all items have the same significance and frequency of oc...
M. Sulaiman Khan, Maybin K. Muyeba, Frans Coenen