Abstract
In this paper, we propose a new cluster-based sample reduction method which is unsupervised, geometric, and density-based. The original data is initially divided into clusters, and each cluster is divided into “portions” defined as the areas between two concentric circles. Then, using the proposed geometric-based formulas, the membership value of each sample belonging to a specific portion is calculated. Samples are then selected from the original data according to the corresponding calculated membership value. We conduct various experiments on the NSL-KDD and KDDCup99 datasets.
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NSL-KKD dataset is available at: http://iscx.ca/NSL-KDD/ (last visit April 2012)
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Mohammadi, M., Raahemi, B., Akbari, A. (2014). A Clustering Density-Based Sample Reduction Method. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_32
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DOI: https://doi.org/10.1007/978-3-319-06483-3_32
Publisher Name: Springer, Cham
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