Simple view
Full metadata view
Authors
Statistics
Online updating of active function cross-entropy clustering
clustering
active function cross-entropy clustering
Gaussian mixture models
data streams
Gaussian mixture models have many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data, since many Gaussian components are typically needed to appropriately fit the data that lie around the nonlinear manifold. To solve this problem, the active function cross-entropy clustering (afCEC) method was constructed. In this article, we present an online afCEC algorithm. Thanks to this modification, we obtain a method which is able to remove unnecessary clusters very fast and, consequently, we obtain lower computational complexity. Moreover, we obtain a better minimum (with a lower value of the cost function). The modification allows to process data streams.
cris.lastimport.wos | 2024-04-09T22:22:31Z | |
dc.abstract.en | Gaussian mixture models have many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data, since many Gaussian components are typically needed to appropriately fit the data that lie around the nonlinear manifold. To solve this problem, the active function cross-entropy clustering (afCEC) method was constructed. In this article, we present an online afCEC algorithm. Thanks to this modification, we obtain a method which is able to remove unnecessary clusters very fast and, consequently, we obtain lower computational complexity. Moreover, we obtain a better minimum (with a lower value of the cost function). The modification allows to process data streams. | pl |
dc.affiliation | Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej | pl |
dc.contributor.author | Spurek, Przemysław - 135993 | pl |
dc.contributor.author | Byrski, Krzysztof - 176317 | pl |
dc.contributor.author | Tabor, Jacek - 132362 | pl |
dc.date.accessioned | 2019-11-22T13:41:28Z | |
dc.date.available | 2019-11-22T13:41:28Z | |
dc.date.issued | 2019 | pl |
dc.date.openaccess | 0 | |
dc.description.accesstime | w momencie opublikowania | |
dc.description.number | 4 | pl |
dc.description.physical | 1409-1425 | pl |
dc.description.version | ostateczna wersja wydawcy | |
dc.description.volume | 22 | pl |
dc.identifier.doi | 10.1007/s10044-018-0701-8 | pl |
dc.identifier.eissn | 1433-755X | pl |
dc.identifier.issn | 1433-7541 | pl |
dc.identifier.project | 2015/19/D/ST6/01472 | pl |
dc.identifier.project | 2017/25/B/ST6/01271 | pl |
dc.identifier.project | ROD UJ / OP | pl |
dc.identifier.uri | https://ruj.uj.edu.pl/xmlui/handle/item/87740 | |
dc.language | eng | pl |
dc.language.container | eng | pl |
dc.rights | Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa | * |
dc.rights.licence | CC-BY | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/legalcode.pl | * |
dc.share.type | inne | |
dc.subject.en | clustering | pl |
dc.subject.en | active function cross-entropy clustering | pl |
dc.subject.en | Gaussian mixture models | pl |
dc.subject.en | data streams | pl |
dc.subtype | Article | pl |
dc.title | Online updating of active function cross-entropy clustering | pl |
dc.title.journal | Pattern Analysis and Applications | pl |
dc.type | JournalArticle | pl |
dspace.entity.type | Publication |
* The migration of download and view statistics prior to the date of April 8, 2024 is in progress.
Views
3
Views per month
Views per city
Downloads
Open Access