Classification via k-means clustering and distance-based outlier detection
Date
2012
ISBN
978-146732314-7
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Journal ISSN
Volume Title
Resource Type
Conference paper
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Journal Title
Classification via k-means clustering and distance-based outlier detection
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Abstract
We propose a two-phase classification method. Specifically, in the first phase, a set of patterns (data) are clustered by the k-means algorithm. In the second phase, outliers are constructed by a distance-based technique and a class label is assigned to each pattern. The Knowledge Discovery Databases (KDD) Cup 1999 data set, which has been utilized extensively for development of intrusion detection systems, is used in our experiment. The results show that the proposed method is effective in intrusion detection. © 2012 IEEE.
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Citation
International Conference on ICT and Knowledge Engineering