The Multiple Pheromone Ant Clustering Algorithm and Its Application to Real World Domains

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Provided by: Institute of Electrical & Electronic Engineers
Topic: Big Data
Format: PDF
The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behavior of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimization approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions.
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