Ant clustering with locally weighted ant perception and diversified memory |
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Authors: | Gilbert L Peterson Christopher B Mayer Thomas L Kubler |
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Institution: | 1. Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH, 45433, USA 2. Department of Electrical and Computer Engineering, United States Naval Academy, M.S. 14B, 105 Maryland Avenue, Annapolis, MD, 21402, USA
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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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