KNCM: Kernel Neutrosophic c-Means clustering

Basit Öğe Kaydı
dc.authorid0000-0003-1614-2639en_US
dc.authorid0000-0002-4760-4843en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0003-1814-9682
dc.contributor.authorAkbulut, Yaman
dc.contributor.authorŞengür, Abdulkadir
dc.contributor.authorGuo, Yanhui
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:48:58Z
dc.date.available2021-06-23T19:48:58Z
dc.date.issued2017
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractData clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as 'neutrosophic c-means' (NCM) was proposed in order to alleviate the limitations of the popular fuzzy c-means (FCM) clustering algorithm by introducing a new objective function which contains two types of rejection. The ambiguity rejection which concerned patterns lying near the cluster boundaries, and the distance rejection was dealing with patterns that are far away from the clusters. In this paper, we extend the idea of NCM for nonlinear-shaped data clustering by incorporating the kernel function into NCM. The new clustering algorithm is called Kernel Neutrosophic c-Means (KNCM), and has been evaluated through extensive experiments. Nonlinear-shaped toy datasets, real datasets and images were used in the experiments for demonstrating the efficiency of the proposed method. A comparison between Kernel FCM (KFCM) and KNCM was also accomplished in order to visualize the performance of both methods. According to the obtained results, the proposed KNCM produced better results than KFCM. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2016.10.001
dc.identifier.endpage724en_US
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85008622705en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage714en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2016.10.001
dc.identifier.urihttps://hdl.handle.net/20.500.12491/9288
dc.identifier.volume52en_US
dc.identifier.wosWOS:000395896500054en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData Clusteringen_US
dc.subjectFuzzy Clusteringen_US
dc.subjectNeutrosophic c-Meansen_US
dc.subjectKernel Functionen_US
dc.titleKNCM: Kernel Neutrosophic c-Means clusteringen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
yaman-akbulut.pdf
Boyut:
6.14 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin/Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu