DGStream: High quality and efficiency stream clustering algorithm

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dc.authoridDalkilic, Gokhan / 0000-0002-0130-1716
dc.authoridAhmed, Rowanda / 0000-0002-5849-3587
dc.authorscopusid57203939278
dc.authorscopusid6507121292
dc.authorscopusid55665216700
dc.authorwosidErten, Yusuf/ABE-9688-2020
dc.authorwosidDalkilic, Gokhan/B-8292-2014
dc.authorwosidAhmed, Rowanda/AAC-7799-2020
dc.contributor.authorAhmed, Rowanda
dc.contributor.authorDalkılıç, Gökhan
dc.contributor.authorErten, Yusuf Murat
dc.date.accessioned2022-02-15T16:57:16Z
dc.date.available2022-02-15T16:57:16Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractRecently as applications produce overwhelming data streams, the need for strategies to analyze and cluster streaming data becomes an urgent and a crucial research area for knowledge discovery. The main objective and the key aim of data stream clustering is to gain insights into incoming data. Recognizing all probable patterns in this boundless data which arrives at varying speeds and structure and evolves over time, is very important in this analysis process. The existing data stream clustering strategies so far, all suffer from different limitations, like the inability to find the arbitrary shaped clusters and handling outliers in addition to requiring some parameter information for data processing. For fast, accurate, efficient and effective handling for all these challenges, we proposed DGStream, a new online-offline grid and density-based stream clustering algorithm. We conducted many experiments and evaluated the performance of DGStream over different simulated databases and for different parameter settings where a wide variety of concept drifts, novelty, evolving data, number and size of clusters and outlier detection are considered. Our algorithm is suitable for applications where the interest lies in the most recent information like stock market, or if the analysis of existing information is required as well as cases where both the old and the recent information are all equally important. The experiments, over the synthetic and real datasets, show that our proposed algorithm outperforms the other algorithms in efficiency. (C) 2019 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2019.112947
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85072608306en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2019.112947
dc.identifier.urihttps://hdl.handle.net/20.500.14034/60
dc.identifier.volume141en_US
dc.identifier.wosWOS:000496334800028en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.journalExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData streams architecturesen_US
dc.subjectData stream miningen_US
dc.subjectGrid-based clusteringen_US
dc.subjectDensity-based clusteringen_US
dc.subjectOnline clusteringen_US
dc.titleDGStream: High quality and efficiency stream clustering algorithmen_US
dc.typeArticleen_US

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