Download
1 / 26
270 likes | 298 Views
Discover the potential of Ant Colony Optimization (ACO) in data mining, including how it works, classification, and clustering. Learn about ACO's origins, algorithm characteristics, and application in solving NP-Complete problems. Explore Data Mining techniques such as Classification and Clustering, along with their applications in decision trees, association rules, and artificial neural networks. Find out how ACO can be combined with Data Mining to create powerful IF-THEN rules for predictive analysis. Dive into a walkthrough of applying ACO to a weather dataset for rule construction and path guidance. Understand how ACO and Data Mining synergy can optimize processes like forming clusters, moving items, and managing pheromone levels in simulations. References: 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.
E N D
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.
