1. A NT C OLONY O PTIMIZATION AND ITS P OTENTIAL IN D ATA M INING By Ben Degler

2. O VERVIEW Ant Colony Optimization How it works Data Mining Classification Clustering

3. A NT C OLONY O PTIMIZATION (ACO) Introduced in early 1990’s Social Insects Swarm Intelligence Classifies ants as collaborative agents Searching for food

4. W HAT IS AN A NT COLONY ? Individual ants Simple Collective Operation Food gathering in the optimal way

5. S EARCHING FOR FOOD Ants leave nest Trail forms Follow trails while they exist

6. S EARCHING C ONTINUED Efficiency Guidance

7. T HE O RIGINAL ACO Marco Dorigo Applied to an NP Complete Problem Approach

8. A LGORITHM C HARACTERISTICS 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. A LGORITHM C HARACTERISTICS Constraint satisfaction Forces construction of feasible rules Fitness Function Pheromone Update Rule

10. D ATA M INING (DM) Availability Multitude of Possibilities New Associations Two Main Techniques Classification Clustering

11. C LASSIFICATION Arrangement The Labeled Model Labeled sets of data Specific attributes

12. M AIN T ECHNIQUES Decision Trees Association Rule K-Nearest Neighbors Algorithm Artificial Neural Networks

13. D ECISION T REE

14. A SSOCIATION R ULES “if CONDITION then PREDICTION”

15. K-N EAREST N EIGHBORS

16. A RTIFICIAL N EURAL N ETWORKS

17. C LUSTERING Unsupervised Learning Unlabeled Data Two Types Hierarchical Non- Hierarchical

18. H IERARCHICAL Dendrogram Merging of Classes

19. N ON -H IERARCHICAL 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:

21. W EATHER D ATASET 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 THEN

22. W EATHER D ATASET Rule construction Applying ACO to the problem Node: Edges: Quality of attribute term Ant constructs a rule Ends in class term node Complete path is a constructed rule

23. W EATHER D ATASET 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. A NT C OLONY S IMULATION …

26. W ORKS C ITED Ioannis Michelakos, Nikolaos Mallios, Elpiniki Papageorgiu, 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.