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

Information Technology and Management Science 2016
Yevgeniy Bodyanskiy, Olena Vynokurova, Ilya Kobylin, Oleg Kobylin

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.

Atslēgas vārdi
Data mining, fuzzy clustering methods, hybrid intelligent systems

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, Vol. 19, 23.-28. lpp. ISSN 2255-9086. e-ISSN 2255-9094.

Publikācijas valoda
English (en)