Adaptive fuzzy clustering with a variable fuzzifier

Kolchygin, B. V.; Bodyanskiy, Yevgeniy

doi:10.1007/s10559-013-9519-y

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…To overcome this drawback, in [15] it was proposed to use the so-called polynomial fuzzifier and a procedure known as fuzzy C-means with polynomial fuzzifier (PFCM). An adaptive online version of the PFCM was introduced in [24] for solving the tasks of Data Stream Mining.…”

Section: Online Data Compression For Reduction Of Initial Feature Spacementioning

confidence: 99%

Online Fuzzy Clustering of High Dimension Data Streams Based on Neural Network Ensembles

Bodyanskiy

Perova

Zhernova

2019

Innovative Technologies and Scientific Solutions for Industries

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The subject matter of the article is fuzzy clustering of high-dimensional data based on the ensemble approach, provided that a number and shape of clusters are not known. The goal of the work is to create the neuro-fuzzy approach for clustering data when the data stream is fed for online processing and a number and shape of clusters are unknown. The following tasks are solved in the article  the input feature space is compressed in the online mode; the model of neural network ensembles for data clustering is built; the ensemble of neuro-fuzzy networks for clustering high-dimensional data is developed; the approach for clustering data in the online mode is worked out. The following results are obtained  the main idea of the proposed approach is based on a modification of the fuzzy C-means algorithm. To reduce the dimension of the input space, the modified Hebb-Sanger network is suggested to be used; this net is characterized by the increased speed and is built on the basis of the modified Oja neurons. A speed-optimized learning algorithm for the Oja neuron is proposed. Such a network implements the method of principal components in the online mode with high speed. Conclusions. In the event the reduction-compression procedure cannot be used due to the probability of losing the physical meaning of the original space, a new clustering criterion was introduced; this criterion contains both a well-known polynomial fuzzifier and the weighment of individual components of the deviations of presented images from cluster centroids. The recurrent modification based on the algorithms proposed in this article is introduced. A mathematical model is developed to determine the quality of clustering with the use of the Xi-Beni index, which was modified for the online mode. The experimental results confirm the fact that the proposed system enables solving a wide range of Data Mining tasks when data sets are processed online, provided that a number and shape of clusters are not known and there is a large number of observations as well.

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Section: Online Data Compression For Reduction Of Initial Feature Spacementioning

confidence: 99%

Online Fuzzy Clustering of High Dimension Data Streams Based on Neural Network Ensembles

Bodyanskiy

Perova

Zhernova

2019

Innovative Technologies and Scientific Solutions for Industries

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“…Thus, the procedure (7) can't be used for solving Data Stream Mining tasks, because it can't process information in an online mode. Therefore, an adaptive modification of the expression (7) was introduced in [22]  …”

Section: Fuzzy Clustering With a Variable Fuzzifiermentioning

confidence: 99%

“…One can avoid this drawback if the ideas of possibilistic fuzzy clustering are used [26]. These ideas are based on minimization of an objective function Introducing an objective function [22,25] similarly to (6)          …”

Section: Possibilistic Fuzzy Clustering With a Variable Fuzzifiermentioning

confidence: 99%

An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

Hu¹,

Bodyanskiy²,

Tyshchenko³

et al. 2016

IJMECS

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Abstract-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. Their learning procedure is carried out with different parameters that define a nature of cluster borders' blurriness. Clusters' quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.

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Self-learning Procedures for a Kernel Fuzzy Clustering System

Bodyanskiy

Tyshchenko

2018

Advances in Intelligent Systems and Computing

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Adaptive fuzzy clustering with a variable fuzzifier

Cited by 8 publications

References 11 publications

Online Fuzzy Clustering of High Dimension Data Streams Based on Neural Network Ensembles

Online Fuzzy Clustering of High Dimension Data Streams Based on Neural Network Ensembles

An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

Self-learning Procedures for a Kernel Fuzzy Clustering System

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