Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases
ACM SIGMOD Record
Fuzzy set technology in knowledge discovery
Fuzzy Sets and Systems
Cure: an efficient clustering algorithm for large databases
Information Systems
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Automated Mining of Fuzzy Association Rules
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Fuzzy summaries in database mining
CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
Mining Fuzzy Quantitative Association Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
Mining Weighted Association Rules for Fuzzy Quantitative Items
Mining Weighted Association Rules for Fuzzy Quantitative Items
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
First approach toward on-line evolution of association rules with learning classifier systems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
The Mahalanobis-Taguchi system - Neural network algorithm for data-mining in dynamic environments
Expert Systems with Applications: An International Journal
An ACS-based framework for fuzzy data mining
Expert Systems with Applications: An International Journal
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
Modeling a dynamic design system using the Mahalanobis Taguchi system: two-step optimal algorithm
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
An improved ant algorithm for fuzzy data mining
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
A multi-level ant-colony mining algorithm for membership functions
Information Sciences: an International Journal
Effect of similar behaving attributes in mining of fuzzy association rules in the large databases
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
An adaptive rule-based approach for managing situation-awareness
Expert Systems with Applications: An International Journal
Detection of fuzzy association rules by fuzzy transforms
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms (2012)
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
MOGA-based fuzzy data mining with taxonomy
Knowledge-Based Systems
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It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets can always be provided by domain experts. Motivated by this, in this paper we propose an automated method for mining fuzzy association rules. For this purpose, we first present a genetic algorithm (GA) based clustering method that adjusts centroids of the clusters, which are to be handled later as midpoints of triangular membership functions. Next, we give a different method for generating the membership functions by using Clustering Using Representatives (CURE) clustering algorithm, which is known as one of the most efficient clustering algorithms described in the literature. Finally, we compared the proposed GA-based approach with other approaches from the literature. Experiments conducted on 100K transactions from the US census in the year 2000 show that the proposed method exhibits a good performance in terms of execution time and interesting fuzzy association rules.