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Ant Colony Optimization and its Potential in Data Mining

Ant Colony Optimization and its Potential in Data Mining. By Ben Degler. Overview. Ant Colony Optimization How it works Data Mining Classification Clustering. Ant Colony Optimization (ACO). Introduced in early 1990’s Social Insects Swarm Intelligence

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Ant Colony Optimization and its Potential in Data Mining

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  1. Ant Colony Optimization and its Potential in Data Mining By Ben Degler

  2. Overview • Ant Colony Optimization • How it works • Data Mining • Classification • Clustering

  3. Ant Colony Optimization (ACO) • Introduced in early 1990’s • Social Insects • Swarm Intelligence • Classifies ants as collaborative agents • Searching for food

  4. What is an Ant colony? • Individual ants • Simple • Collective Operation • Food gathering in the optimal way

  5. Searching for food • Ants leave nest • Trail forms • Follow trails while they exist

  6. Searching Continued • Efficiency • Guidance

  7. The Original ACO • Marco Dorigo • Applied to an NP Complete Problem • Approach

  8. Algorithm Characteristics • Appropriate Problem Representation • Move from one city to another until tour is completed • Local heuristic • Trails building • Transition Rule • Independent of heuristic value and pheromone level

  9. Algorithm Characteristics • Constraint satisfaction • Forces construction of feasible rules • Fitness Function • Pheromone Update Rule

  10. Data Mining (DM) • Availability • Multitude of Possibilities • New Associations • Two Main Techniques • Classification • Clustering

  11. Classification • Arrangement • The Labeled Model • Labeled sets of data • Specific attributes

  12. Main Techniques • Decision Trees • Association Rule • K-Nearest Neighbors Algorithm • Artificial Neural Networks

  13. Decision Tree

  14. Association Rules • “if CONDITION then PREDICTION”

  15. K-Nearest Neighbors

  16. Artificial Neural Networks

  17. Clustering • Unsupervised Learning • Unlabeled Data • Two Types • Hierarchical • Non-Hierarchical

  18. Hierarchical • Dendrogram • Merging of Classes

  19. Non-Hierarchical • Focuses on subclasses • Uses the k-means algorithm

  20. ACO + DM • ACO algorithms in the form of IF-THEN • IF(Conditions) THEN(class) • Conditions: (term_1) AND (term_2) AND … AND (term_n) • Each term is a triple (attribute, operator, value) • EX: <smoke=no>

  21. Weather Dataset • Are we able to play outside today? • Play{yes, no} • Four predicting attributes • Outlook{sunny, overcast, rainy} • Temperature{hot, mild, cold} • Humidity{high, normal} • Windy{true, false} • IF<humidity=normal>THEN<yes>

  22. Weather Dataset • Rule construction • Applying ACO to the problem • Node: <humidity=normal> • Edges: Quality of attribute term • Ant constructs a rule • Ends in class term node • <play=yes> • Complete path is a constructed rule

  23. Weather Dataset • Path Quality • Node Quality • Guidance

  24. ACO + DC • Ability to form piles • Cluster dead bodies • Simple and complex movements • Probability of moving items • Pheromone levels

  25. Ant Colony Simulation • …

  26. Works Cited IoannisMichelakos, NikolaosMallios, ElpinikiPapageorgiu, Michael Vassilakopoulos, “Ant Colony Optimization and Data Mining: Techniques and Trends”, International Conference on P2P, Parallel Grid and Cloud Computing, IEEE Computer Society, pp. 284-286, 2010.

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