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Ant Colony Algorithms for Data Learning

Author

Listed:
  • Mohamed Hamlich

    (Computer Science Lab, UH2, FSTM, Mohammadia, Morocco)

  • Mohammed Ramdani

    (Computer Science Lab, UH2, FSTM, Mohammadia, Morocco)

Abstract

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.

Suggested Citation

  • Mohamed Hamlich & Mohammed Ramdani, 2013. "Ant Colony Algorithms for Data Learning," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 4(3), pages 1-10, July.
    • Handle: RePEc:igg:jaec00:v:4:y:2013:i:3:p:1-10
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