MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data

Basit Öğe Kaydı
dc.authoridSENOL, Ali/0000-0003-0364-2837
dc.authoridKAYA, Mahmut/0000-0002-7846-1769
dc.contributor.authorErdinc, Berfin
dc.contributor.authorKaya, Mahmut
dc.contributor.authorSenol, Ali
dc.date.accessioned2024-12-24T19:24:26Z
dc.date.available2024-12-24T19:24:26Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractStream clustering has emerged as a vital area for processing streaming data in real-time, facilitating the extraction of meaningful information. While efficient approaches for defining and updating clusters based on similarity criteria have been proposed, outliers and noisy data within stream clustering areas pose a significant threat to the overall performance of clustering algorithms. Moreover, the limitation of existing methods in generating non-spherical clusters underscores the need for improved clustering quality. As a new methodology, we propose a new stream clustering approach, MCMSTStream, to overcome the abovementioned challenges. The algorithm applies MST to micro-clusters defined by using the KD-Tree data structure to define macro-clusters. MCMSTStream is robust against outliers and noisy data and has the ability to define clusters with arbitrary shapes. Furthermore, the proposed algorithm exhibits notable speed and can handling high-dimensional data. ARI and Purity indices are used to prove the clustering success of the MCMSTStream. The evaluation results reveal the superior performance of MCMSTStream compared to state-of-the-art stream clustering algorithms such as DenStream, DBSTREAM, and KD-AR Stream. The proposed method obtained a Purity value of 0.9780 and an ARI value of 0.7509, the highest scores for the KDD dataset. In the other 11 datasets, it obtained much higher results than its competitors. As a result, the proposed method is an effective stream clustering algorithm on datasets with outliers, high-dimensional, and arbitrary-shaped clusters. In addition, its runtime performance is also quite reasonable.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK)
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK).
dc.identifier.doi10.1007/s00521-024-09443-1
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.scopus2-s2.0-85186204453
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s00521-024-09443-1
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5991
dc.identifier.wosWOS:001171299200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectStream clustering
dc.subjectMicro-cluster
dc.subjectMinimum spanning tree
dc.titleMCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu