Overcoming key weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity
published: Sept. 25, 2016, recorded: August 2016, views: 1411
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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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