Participatory Learning Fuzzy Clustering For Interval-valued Data

dc.creatorMaciel
dc.creatorLeandro; Ballini
dc.creatorRosangela; Gomide
dc.creatorFernando; Yager
dc.creatorRonald R.
dc.date2016
dc.date2017-11-13T13:25:01Z
dc.date2017-11-13T13:25:01Z
dc.date.accessioned2018-03-29T05:57:29Z
dc.date.available2018-03-29T05:57:29Z
dc.identifier978-3-319-40596-4; 978-3-319-40595-7
dc.identifierInformation Processing And Management Of Uncertainty In Knowledge-based Systems, Ipmu 2016, Pt I. Springer Int Publishing Ag, v. 610, p. 687 - 698, 2016.
dc.identifier1865-0929
dc.identifierWOS:000389515800057
dc.identifier10.1007/978-3-319-40596-4_57
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-319-40596-4_57
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/328427
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1365452
dc.descriptionThis paper suggests an interval participatory learning fuzzy clustering (iPL) method for partitioning interval-valued data. Participatory learning provides a paradigm for learning that emphasizes the pervasive role of what is already known or believed in the learning process. iPL clustering method uses interval arithmetic, and the Hausdorff distance to compute the (dis) similarity between intervals. Computational experiments are reported using synthetic interval data sets with linearly non-separable clusters of different shapes and sizes. Comparisons include traditional hard and fuzzy clustering techniques for interval-valued data as benchmarks in terms of corrected Rand (CR) index for comparing two partitions. The results suggest that the interval participatory learning fuzzy clustering algorithm is highly effective to cluster interval-valued data and has comparable performance than alternative hard and fuzzy interval-based approaches.
dc.description610
dc.description687
dc.description698
dc.description16th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU)
dc.descriptionJUN 20-24, 2016
dc.descriptionEindhoven, NETHERLANDS
dc.languageEnglish
dc.publisherSpringer Int Publishing AG
dc.publisherCham
dc.relationInformation Processing And Management of Uncertainty In Knowledge-Based Systems, IPMU 2016, PT I
dc.rightsfechado
dc.sourceWOS
dc.subjectFuzzy Clustering
dc.subjectParticipatory Learning
dc.subjectInterval Data
dc.titleParticipatory Learning Fuzzy Clustering For Interval-valued Data
dc.typeActas de congresos


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