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ICDE
2006
IEEE
130views Database» more  ICDE 2006»
14 years 11 months ago
MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining
We propose a framework, called MIC, which adopts an information-theoretic approach to address the problem of quantitative association rule mining. In our MIC framework, we first d...
Yiping Ke, James Cheng, Wilfred Ng
IEAAIE
2009
Springer
14 years 4 months ago
Incremental Mining of Ontological Association Rules in Evolving Environments
The process of knowledge discovery from databases is a knowledge intensive, highly user-oriented practice, thus has recently heralded the development of ontology-incorporated data ...
Ming-Cheng Tseng, Wen-Yang Lin
BMCBI
2008
171views more  BMCBI 2008»
13 years 10 months ago
Fuzzy association rules for biological data analysis: A case study on yeast
Background: Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the info...
Francisco J. Lopez, Armando Blanco, Fernando Garci...
ISI
2005
Springer
14 years 3 months ago
Private Mining of Association Rules
This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among two parti...
Justin Z. Zhan, Stan Matwin, LiWu Chang
PAKDD
2005
ACM
94views Data Mining» more  PAKDD 2005»
14 years 3 months ago
Progressive Sampling for Association Rules Based on Sampling Error Estimation
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. S...
Kun-Ta Chuang, Ming-Syan Chen, Wen-Chieh Yang