Abstract
We propose a new class of distance measures (metrics) designed for multisets, both of which are a recurrent theme in many data mining applications. One particular instance of this class originated from the necessity for a clustering of criminal behaviours.
These distance measures are parameterized by a function f which, given a few simple restrictions, will always produce a valid metric. This flexibility allows these measures to be tailored for many domain-specific applications.
In this paper, the metrics are applied in bio-informatics (genomics), criminal behaviour clustering and text mining. The metric we propose also is a generalization of some known measures, e.g., the Jaccard distance and the Canberra distance. We discuss several options, and compare the behaviour of different instances.
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Kosters, W.A., Laros, J.F.J. (2008). Metrics for Mining Multisets. In: Bramer, M., Coenen, F., Petridis, M. (eds) Research and Development in Intelligent Systems XXIV. SGAI 2007. Springer, London. https://doi.org/10.1007/978-1-84800-094-0_22
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DOI: https://doi.org/10.1007/978-1-84800-094-0_22
Publisher Name: Springer, London
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