Document Type
Article
Publication Date
3-2008
Abstract
Ant clustering algorithms are a robust and flexible tool for clustering data that have produced some promising results. This paper introduces two improvements that can be incorporated into any ant clustering algorithm: kernel function similarity weights and a similarity memory model replacement scheme. A kernel function weights objects within an ant’s neighborhood according to the object distance and provides an alternate interpretation of the similarity of objects in an ant’s neighborhood. Ants can hill-climb the kernel gradients as they look for a suitable place to drop a carried object. The similarity memory model equips ants with a small memory consisting of a sampling of the current clustering space. We test several kernel functions and memory replacement schemes on the Iris, Wisconsin Breast Cancer, and Lincoln Lab network intrusion datasets. Compared to a basic ant clustering algorithm, we show that kernel functions and the similarity memory model increase clustering speed and cluster quality, especially for datasets with an unbalanced class distribution, such as network intrusion.
DOI
10.1007/s11721-008-0011-7
Source Publication
Swarm Intelligence
Recommended Citation
Peterson, G. L., Mayer, C. B., & Kubler, T. L. (2008). Ant clustering with locally weighted ant perception and diversified memory. Swarm Intelligence, 2(1), 43–68. https://doi.org/10.1007/s11721-008-0011-7
Comments
© Springer Science + Business Media, LLC 2008
AFIT Scholar furnishes the draft version of this article. The published version of record appears in volume 2 of Swarm Intelligence and is available by subscription through the DOI link in the citation below.
A read-only screen view of the published article is available through the SpringerNature SharedIt content sharing program by clicking here.