Overcoming key weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity

author: Kai Ming Ting, Federation University Australia
published: Sept. 25, 2016,   recorded: August 2016,   views: 1418

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This paper introduces the first generic version of data dependent dissimilarity and shows that it provides a better closest match than distance measures for three existing algorithms in clustering, anomaly detection and multi-label classification. For each algorithm, we show that by simply replacing the distance measure with the data dependent dissimilarity measure, it overcomes a key weakness of the otherwise unchanged algorithm.

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