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Ant Colony Optimization and its Potential in Data Mining

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## Ant Colony Optimization and its Potential in Data Mining

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**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 • Classifies ants as collaborative agents • Searching for food**What is an Ant colony?**• Individual ants • Simple • Collective Operation • Food gathering in the optimal way**Searching for food**• Ants leave nest • Trail forms • Follow trails while they exist**Searching Continued**• Efficiency • Guidance**The Original ACO**• Marco Dorigo • Applied to an NP Complete Problem • Approach**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**Algorithm Characteristics**• Constraint satisfaction • Forces construction of feasible rules • Fitness Function • Pheromone Update Rule**Data Mining (DM)**• Availability • Multitude of Possibilities • New Associations • Two Main Techniques • Classification • Clustering**Classification**• Arrangement • The Labeled Model • Labeled sets of data • Specific attributes**Main Techniques**• Decision Trees • Association Rule • K-Nearest Neighbors Algorithm • Artificial Neural Networks**Association Rules**• “if CONDITION then PREDICTION”**Clustering**• Unsupervised Learning • Unlabeled Data • Two Types • Hierarchical • Non-Hierarchical**Hierarchical**• Dendrogram • Merging of Classes**Non-Hierarchical**• Focuses on subclasses • Uses the k-means algorithm**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>**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>**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**Weather Dataset**• Path Quality • Node Quality • Guidance**ACO + DC**• Ability to form piles • Cluster dead bodies • Simple and complex movements • Probability of moving items • Pheromone levels**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.