Data stream analysis

dc.contributor	Graduate Program in Computational Science and Engineering.
dc.contributor.advisor	Ecevit, Fatih.
dc.contributor.advisor	Kaygun, Atabey.
dc.contributor.author	Çalışkan, Mine Melodi.
dc.date.accessioned	2023-03-16T10:03:39Z
dc.date.available	2023-03-16T10:03:39Z
dc.date.issued	2018.
dc.identifier.other	CSE 2018 C36
dc.identifier.uri	http://digitalarchive.boun.edu.tr/handle/123456789/12364
dc.description.abstract	In this thesis we give a survey of online machine learning algorithms for data stream analysis. After giving an overview of standard batch algorithms, we explain batch-to-online conversion, and we give a in-depth description and analysis of data stream mining techniques. We particularly focus on online k-means algorithms and multilayer perceptron models as representative examples of online clustering and clas siﬁcation algorithms. We also present theoretical and empirical analyses of diﬀerent approaches for online versions of these algorithms through numerical experiments.
dc.format.extent	30 cm.
dc.publisher	Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2018.
dc.subject.lcsh	Data analysis.
dc.title	Data stream analysis
dc.format.pages	xv, 113 leaves ;