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Ant clustering with locally weighted ant perception and diversified memory

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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.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH, 45433, USA

    Gilbert L. Peterson & Thomas L. Kubler

  2. Department of Electrical and Computer Engineering, United States Naval Academy, M.S. 14B, 105 Maryland Avenue, Annapolis, MD, 21402, USA

    Christopher B. Mayer

Authors
  1. Gilbert L. Peterson
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  2. Christopher B. Mayer
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  3. Thomas L. Kubler
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Correspondence to Gilbert L. Peterson.

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The views expressed herein are those of the authors and do not reflect the official policy or position of the US Air Force, Dept. of Defense, or the US Government. The US Government may reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation here on. This paper was supported by the Air Force Office of Scientific Research.

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Peterson, G.L., Mayer, C.B. & Kubler, T.L. Ant clustering with locally weighted ant perception and diversified memory. Swarm Intell 2, 43–68 (2008). https://doi.org/10.1007/s11721-008-0011-7

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  • DOI: https://doi.org/10.1007/s11721-008-0011-7

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