An improved ant algorithm for fuzzy data mining

Authors:
Min-Thai Wu;Tzung-Pei Hong;Chung-Nan Lee
Affiliations:
Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan;Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan and Department of Computer Science and Information Engineering, National University of Kaohisung, ...;Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
Venue:
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Year:
2010

Citing 11
Cited 1

Fuzzy expert systems

Fuzzy expert 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
MAX-MIN Ant system

Future Generation Computer Systems
Finding useful fuzzy concepts for pattern classification using genetic algorithm

Information Sciences: an International Journal
A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Soft Computing - A Fusion of Foundations, Methodologies and Applications
An ACS-based framework for fuzzy data mining

Expert Systems with Applications: An International Journal
Genetic algorithm based framework for mining fuzzy association rules

Fuzzy Sets and Systems
A fuzzy rule based backpropagation method for training binary multilayer perceptrons

Information Sciences: an International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization

IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Preventing attacks by classifying user models in a collaborative scenario

ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the past, two mining algorithms were proposed to find suitable membership functions for fuzzy association rules based on the ant colony systems. In the two approaches, the coding of the possible solutions is by binary strings, which form a discrete solution space. The paper extends the original approaches to continuous search space, and a fuzzy mining algorithm based on the improved ant approach is proposed. The improved ant approach doesn't have fixed paths and nodes and produces some paths in a dynamic way according to the distribution functions of pheromones. The experimental results show that the mining process based on the improved ant approach gets better results than that based on the previous two algorithms.