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TEC
2008
104views more  TEC 2008»
13 years 6 months ago
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data i...
Tzung-Pei Hong, Chun-Hao Chen, Yeong-Chyi Lee, Yu-...
FUZZIEEE
2007
IEEE
14 years 1 months ago
A Genetic-Fuzzy Mining Approach for Items with Multiple Minimum Supports
—In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions under a single minim...
Chun-Hao Chen, Tzung-Pei Hong, Vincent S. Tseng, C...
ICDT
2009
ACM
119views Database» more  ICDT 2009»
14 years 7 months ago
Analysis of sampling techniques for association rule mining
In this paper, we present a comprehensive theoretical analysis of the sampling technique for the association rule mining problem. Most of the previous works have concentrated only...
Venkatesan T. Chakaravarthy, Vinayaka Pandit, Yogi...
ICDE
2009
IEEE
192views Database» more  ICDE 2009»
14 years 8 months ago
Deriving Private Information from Association Rule Mining Results
Data publishing can provide enormous benefits to the society. However, due to privacy concerns, data cannot be published in their original forms. Two types of data publishing can a...
Zutao Zhu, Guan Wang, Wenliang Du
JCST
2008
119views more  JCST 2008»
13 years 6 months ago
Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy
This paper presents some new algorithms to efficiently mine max frequent generalized itemsets (g-itemsets) and essential generalized association rules (g-rules). These are compact ...
Daniel Kunkle, Donghui Zhang, Gene Cooperman