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Ant Colony Optimization and its Potential in Data Mining

Explore the potential of Ant Colony Optimization (ACO) in data mining with a focus on classification, clustering, and the synergy of ACO algorithms. Learn how ACO works, its application in solving NP-complete problems, and its integration with data mining techniques for rule construction.

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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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