scholarly journals Mining Multiple-level Association Rules Based on Pre-large Concepts

10.5772/6448 ◽  
2009 ◽  
Author(s):  
Tzung-Pei Hong ◽  
Tzu-Jung Huang ◽  
Chao-Sheng Chang
Keyword(s):  
Association Rules ◽  
Multiple Level

GENETIC-FUZZY MINING WITH TAXONOMY

2012 ◽  
Vol 20 (supp02) ◽  
pp. 187-205 ◽  
Author(s):  
CHUN-HAO CHEN ◽  
TZUNG-PEI HONG ◽  
YEONG-CHYI LEE
Keyword(s):  
Association Rules ◽  
Multiple Level ◽  
Membership Functions ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Fitness Value ◽  
Real World Applications ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining ◽  
The Given

Data mining is most commonly used in attempts to induce association rules from transaction data. Since transactions in real-world applications usually consist of quantitative values, many fuzzy association-rule mining approaches have been proposed on single- or multiple-concept levels. However, the given membership functions may have a critical influence on the final mining results. In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rules using multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different concept levels and the suitability of membership functions in the chromosome. After the GA process terminates, a better set of multiple-level fuzzy association rules can then be expected with a more suitable set of membership functions. Experimental results on a simulation dataset also show the effectiveness of the algorithm.

scholarly journals Efficient Method for Mining Multiple-Level and Generalized Association Rules

2011 ◽  
Vol 22 (12) ◽  
pp. 2965-2980 ◽  
Author(s):  
Yu-Xing MAO ◽  
Tong-Bing CHEN ◽  
Bai-Le SHI
Keyword(s):  
Association Rules ◽  
Efficient Method ◽  
Multiple Level ◽  
Generalized Association Rules
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