The multiple pheromone ant clustering algorithm and its application to real world domains

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation 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. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models.
Original languageEnglish
Title of host publication2013 Federated conference on Computer Science and Information Systems, FedCSIS 2013
PublisherIEEE
Pages27-34
Number of pages8
ISBN (Print)978-1-4673-4471-5
Publication statusPublished - 2013
Event2013 Federated conference on Computer Science and Information Systems - Kraków, Poland
Duration: 8 Sep 201311 Sep 2013

Conference

Conference2013 Federated conference on Computer Science and Information Systems
Abbreviated titleFedCSIS 2013
CountryPoland
CityKraków
Period8/09/1311/09/13

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  • Research Output

    • 1 Conference contribution

    Designing multiple user perspectives and functionality for clinical decision support systems

    Buckingham, C. D., Ahmed, A. & Adams, A., 2013, 2013 Federated conference on Computer Science and Information Systems, FedCSIS 2013. IEEE, p. 211-218 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference publication

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