Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning
Ant Colony Optimization
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Clustering
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A new classification-rule pruning procedure for an ant colony algorithm
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
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Fuzzy Ant-Miner algorithm processes data with nominal class and has the disadvantage of not treating the data with continuous class. In this paper, after presenting the Fuzzy Ant Miner algorithm, the authors propose a new learning method to partition heterogeneous data with continuous class. This method in a first step finds the optimal path between the data using algorithms of ants. Distance adopted in their optimization method takes into account all types of data. The second step vise to divide the data into homogeneous groups by browsing the optimal path found. A new test probability is estimated based on the distance and the amount of pheromone deposited by ants in the transitions between the data. A third step is to find the prototype of each cluster to identify the cluster membership of any new data injected.