dc.creatorFaceli, Katti
dc.creatorSakata, Tiemi C.
dc.creatorCarvalho, André Carlos Ponce de Leon Ferreira de
dc.creatorSouto, Marcilio C. P. de
dc.date.accessioned2014-11-10T16:54:56Z
dc.date.accessioned2018-07-04T16:52:40Z
dc.date.available2014-11-10T16:54:56Z
dc.date.available2018-07-04T16:52:40Z
dc.date.created2014-11-10T16:54:56Z
dc.date.issued2014-07
dc.identifierInternational Joint Conference on Neural Networks, 2014, Beijing.
dc.identifier9781479914845
dc.identifierhttp://www.producao.usp.br/handle/BDPI/46570
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2014.6889672
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1641589
dc.description.abstractRecent advances in cluster analysis highlight the importance of finding multiple meaningful partitions and point out to the need for approaches to evaluate them. They also suggest that the evaluation should consider knowledge of a domain expert. In this paper, we present a visualization method, called PVis1 (Partition's Visualizer), that allows the integrated visualization of a collection of partitions. PVis allows to compare the content of a set of partitions. The comparison can be done with respect to priori knowledge provided by an expert. PVis can be useful in the discovery of relevant information to the domain experts performing cluster analysis. In order to illustrate our approach, we give an example of how to perform an exploratory analysis of collections of partitions. In order to do so, we use a well-known dataset from the Bioinformatics domain, regarding molecular classification of cancer.
dc.languageeng
dc.publisherIEEE Computational Intelligence Society
dc.publisherChinese Academy of Sciences
dc.publisherNational Natural Science Foundation of China
dc.publisherBeijing
dc.relationInternational Joint Conference on Neural Networks
dc.rightsCopyright IEEE
dc.rightsrestrictedAccess
dc.titlePVis - Partitions’ Visualizer: extracting knowledge by visualizing a collection of partitions
dc.typeActas de congresos


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