Mining flexible multiple-level association rules in all concept hierarchies

1998 ◽  
pp. 786-795 ◽  
Author(s):  
Li Shen ◽  
Hong Shen
Keyword(s):  
Association Rules ◽  
Multiple Level ◽  
Concept Hierarchies

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

MINING ASSOCIATION RULES FROM MARKET BASKET DATA USING SHARE MEASURES AND CHARACTERIZED ITEMSETS

1998 ◽  
Vol 07 (02) ◽  
pp. 189-220 ◽  
Author(s):  
ROBERT J. HILDERMAN ◽  
HOWARD J. HAMILTON ◽  
COLIN L. CARTER ◽  
NICK CERCONE
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
A Priori ◽  
Experimental Results ◽  
Market Basket ◽  
Large Databases ◽  
Mining Association Rules ◽  
Customer Profiles ◽  
Concept Hierarchies

We propose the share-confidence framework for knowledge discovery from databases which addresses the problem of mining characterized association rules from market basket data (i.e., itemsets). Our goal is to not only discover the buying patterns of customers, but also to discover customer profiles by partitioning customers into distinct classes. We present a new algorithm for classifying itemsets based upon characteristic attributes extracted from census or lifestyle data. Our algorithm combines the A priori algorithm for discovering association rules between items in large databases, and the A O G algorithm for attribute-oriented generalization in large databases. We show how characterized itemsets can be generalized according to concept hierarchies associated with the characteristic attributes. Finally, we present experimental results that demonstrate the utility of the share-confidence framework.

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