Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Mining fuzzy association rules in databases
ACM SIGMOD Record
Fuzzy set technology in knowledge discovery
Fuzzy Sets and Systems
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Cure: an efficient clustering algorithm for large databases
Information Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
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
Semi-parametric optimization for missing data imputation
Applied Intelligence
An Integration of Fuzzy Association Rules and WordNet for Document Clustering
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining fuzzy frequent itemsets for hierarchical document clustering
Information Processing and Management: an International Journal
Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Integrated Computer-Aided Engineering
Data & Knowledge Engineering
A case-based classifier for hypertension detection
Knowledge-Based Systems
Building a highly-compact and accurate associative classifier
Applied Intelligence
International Journal of Approximate Reasoning
An Optimum Vertical Handoff Decision Algorithm Based on Adaptive Fuzzy Logic and Genetic Algorithm
Wireless Personal Communications: An International Journal
A genetic design of linguistic terms for fuzzy rule based classifiers
International Journal of Approximate Reasoning
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
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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. In general, it is unrealistic that experts can always provide such sets. And finding the most appropriate fuzzy sets becomes a more complex problem when items are not considered to have equal importance and the support and confidence parameters required for the association rules mining process are specified as linguistic terms. Existing clustering based automated methods are not satisfactory because they do not consider the optimization of the discovered membership functions. In order to tackle this problem, we propose Genetic Algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit based on user specified linguistic minimum support and confidence terms. This is achieved by tuning the base values of the membership functions for each quantitative attribute with respect to two different evaluation functions maximizing the number of large itemsets and the average of the confidence intervals of the generated rules. To the best of our knowledge, this is the first effort in this direction. Experiments conducted on 100 K transactions from the adult database of United States census in year 2000 demonstrate that the proposed clustering method exhibits good performance in terms of the number of produced large itemsets and interesting association rules.