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dc.contributor.authorCsenki, Attila
dc.contributor.authorNeagu, Daniel
dc.contributor.authorTorgunov, Denis
dc.contributor.authorMicic, Natasha
dc.date.accessioned2020-01-11T16:21:33Z
dc.date.accessioned2020-01-22T14:19:53Z
dc.date.available2020-01-11T16:21:33Z
dc.date.available2020-01-22T14:19:53Z
dc.date.issued2020
dc.identifier.citationCsenki A, Neagu D, Torgunov D et al (2020) Proximity curves for potential-based clustering. Journal of Classification. 37: 671-695.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17596
dc.descriptionYesen_US
dc.description.abstractThe concept of proximity curve and a new algorithm are proposed for obtaining clusters in a finite set of data points in the finite dimensional Euclidean space. Each point is endowed with a potential constructed by means of a multi-dimensional Cauchy density, contributing to an overall anisotropic potential function. Guided by the steepest descent algorithm, the data points are successively visited and removed one by one, and at each stage the overall potential is updated and the magnitude of its local gradient is calculated. The result is a finite sequence of tuples, the proximity curve, whose pattern is analysed to give rise to a deterministic clustering. The finite set of all such proximity curves in conjunction with a simulation study of their distribution results in a probabilistic clustering represented by a distribution on the set of dendrograms. A two-dimensional synthetic data set is used to illustrate the proposed potential-based clustering idea. It is shown that the results achieved are plausible since both the ‘geographic distribution’ of data points as well as the ‘topographic features’ imposed by the potential function are well reflected in the suggested clustering. Experiments using the Iris data set are conducted for validation purposes on classification and clustering benchmark data. The results are consistent with the proposed theoretical framework and data properties, and open new approaches and applications to consider data processing from different perspectives and interpret data attributes contribution to patterns.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/s00357-019-09348-yen_US
dc.rights© The Author(s) 2019. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.subjectClusteringen_US
dc.subjectPhysical modelen_US
dc.subjectAnisotropic potentialen_US
dc.subjectCauchy class of distributionsen_US
dc.subjectSteepest descenten_US
dc.subjectProbabilistic dendogramen_US
dc.subjectProximity curveen_US
dc.subjectIris data seten_US
dc.titleProximity curves for potential-based clusteringen_US
dc.status.refereedYesen_US
dc.date.Accepted2019
dc.date.application2019-12-18
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.date.updated2020-01-11T16:21:48Z
refterms.dateFOA2020-01-22T14:20:25Z


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