Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Fuzzy connectives based crossover operators to model genetic algorithms population diversity
Fuzzy Sets and Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Fuzzy Classifier Systems
Proceedings of the 5th International Conference on Genetic Algorithms
A New Approach on the Traveling Salesman Problem by Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases
SBAC-PAD '03 Proceedings of the 15th Symposium on Computer Architecture and High Performance Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Data & Knowledge Engineering
Intrusion detection using fuzzy association rules
Applied Soft Computing
Mining direct and indirect weighted fuzzy association rules in large transaction databases
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Integrating fuzzy knowledge by genetic algorithms
IEEE Transactions on Evolutionary Computation
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
SNPD '12 Proceedings of the 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Hi-index | 12.05 |
Data mining is most commonly used in attempts to induce association rules from transaction data. In the past, we used the fuzzy and GA concepts to discover both useful fuzzy association rules and suitable membership functions from quantitative values. The evaluation for fitness values was, however, quite time-consuming. Due to dramatic increases in available computing power and concomitant decreases in computing costs over the last decade, learning or mining by applying parallel processing techniques has become a feasible way to overcome the slow-learning problem. In this paper, we thus propose a parallel genetic-fuzzy mining algorithm based on the master-slave architecture to extract both association rules and membership functions from quantitative transactions. The master processor uses a single population as a simple genetic algorithm does, and distributes the tasks of fitness evaluation to slave processors. The evolutionary processes, such as crossover, mutation and production are performed by the master processor. It is very natural and efficient to run the proposed algorithm on the master-slave architecture. The time complexities for both sequential and parallel genetic-fuzzy mining algorithms have also been analyzed, with results showing the good effect of the proposed one. When the number of generations is large, the speed-up can be nearly linear. The experimental results also show this point. Applying the master-slave parallel architecture to speed up the genetic-fuzzy data mining algorithm is thus a feasible way to overcome the low-speed fitness evaluation problem of the original algorithm.