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

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.


Data mining; fuzzy clustering methods; hybrid intelligent systems

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