Zhengbing Hu

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko et al.

A task of clustering data given in the ordinal scale under conditions of overlapping clusters has been considered. It's proposed to use an approach based on memberhsip and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko et al.

A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko et al.

A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko et al.

An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.

Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko et al.

Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.