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Cluster validation in clustering-based one-class classification
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DOI：10.1111/exsy.12475.png)
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Reconstruction-based one-class classification has shown to be very effective in a number of domains. This approach works by attempting to capture the underlying structure of the normal class, typically, by means of clusters of objects. It has the main disadvantage, however, that one has to indicate the number of clusters in advance, for this yields an efficient way of computing a clustering. In this paper, we introduce a new algorithm, OCKRA++, which achieves a better performance, by enhancing a clustering-based one-class ensemble classifier (OCKRA) with a cluster validity index that is used to set the best number of clusters during the classifier's training process. We have thoroughly tested OCKRA++ in a particular domain, namely masquerade detection. For this purpose, we have used the Windows-Users and -Intruder simulation Logs data set repository, which contains 70 different masquerade data sets. We have found that OCKRA++ is currently the algorithm that achieves the best area under the curve , with a significant difference, in masquerade detection using the file system navigation approach.
Keyword:
clustering
masquerade detection
one-class classification
validity index
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Bagging-TPMiner: a classifier ensemble for masquerader detection based on typical objects.png)
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