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Publikācija: Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks

Publication Type Publication (anonimusly reviewed) in a journal with an international editorial board indexed in other databases
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Yevgeniy Bodyanskiy
Olena Vynokurova
Ilya Kobylin
Oleg Kobylin
Keywords Data mining, fuzzy clustering methods, hybrid intelligent systems
Abstract In the paper, adaptive modifications of fuzzy clustering methods have been proposed for solving the problem of data stream mining in online mode. The clustering-segmentation task of short time series with unevenly distributed observations (at the same time in all samples) is considered. The proposed approach for adaptive fuzzy clustering of data stream is sufficiently simple in numerical implementation and is characterised by a high speed of information processing. The computational experiments have confirmed the effectiveness of the developed approach.
DOI: 10.1515/itms-2016-0006
Reference Bodyanskiy, Y., Vynokurova, O., Kobylin, I., Kobylin, O. Adaptive Fuzzy Clustering of Short Time Series with Unevenly Distributed Observations in Data Stream Mining Tasks. Information Technology and Management Science, 2016, 19, pp.23-28. ISSN 2255-9086. e-ISSN 2255-9094. Available from: doi:10.1515/itms-2016-0006
ID 23717