ACO algorithms for the quadratic assignment problem
New ideas in optimization
Integrating membership functions and fuzzy rule sets from multiple knowledge sources
Fuzzy Sets and Systems
An Ants heuristic for the frequency assignment problem
Future Generation Computer Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Genetic algorithm based framework for mining fuzzy association rules
Fuzzy Sets and Systems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Integrating fuzzy knowledge by genetic algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
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
International Journal of Business Intelligence and Data Mining
Hi-index | 12.05 |
Data mining is often used to find out interesting and meaningful patterns from huge databases. It may generate different kinds of knowledge such as classification rules, clusters, association rules, and among others. A lot of researches have been proposed about data mining and most of them focused on mining from binary-valued data. Fuzzy data mining was thus proposed to discover fuzzy knowledge from linguistic or quantitative data. Recently, ant colony systems (ACS) have been successfully applied to optimization problems. However, few works have been done on applying ACS to fuzzy data mining. This thesis thus attempts to propose an ACS-based framework for fuzzy data mining. In the framework, the membership functions are first encoded into binary-bits and then fed into the ACS to search for the optimal set of membership functions. The problem is then transformed into a multi-stage graph, with each route representing a possible set of membership functions. When the termination condition is reached, the best membership function set (with the highest fitness value) can then be used to mine fuzzy association rules from a database. At last, experiments are made to make a comparison with other approaches and show the performance of the proposed framework.