期刊全称 | Big Data: Conceptual Analysis and Applications | 影响因子2023 | Michael Z. Zgurovsky,Yuriy P. Zaychenko | 视频video | | 发行地址 | Applies methods of modern mathematics and system analytics to the analysis of big data.Extracts hidden regularities from these data.Presents conventional tools of data mining and novel efficient metho | 学科分类 | Studies in Big Data | 图书封面 |  | 影响因子 | .The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity... . | Pindex | Book 2020 |
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Front Matter |
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Abstract
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,The Cluster Analysis in Big Data Mining, |
Michael Z. Zgurovsky,Yuriy P. Zaychenko |
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Abstract
The purpose of this chapter is the consideration of modern methods of the cluster analysis, crisp methods and fuzzy methods, robust probabilistic and possibilistic clustering methods, their properties and application. The problem of cluster analysis is formulated, main criteria and metrics are considered and discussed. Classification of cluster analysis methods is presented, several crisp methods are considered, in particular, hard C-means method and Ward’s method. Fuzzy clustering methods are considered and analyzed: fuzzy C-means method and its generalization Gustavsson-Kessel’s method of cluster analysis which is used when metrics of distance differs from Euclidian. The methods of initial location of cluster centers are considered: peak and differential grouping and their properties analyzed. Adaptive robust clustering algorithms are presented and analyzed which are used when initial data is distorted by high level of noise, or by outliers. In the Sect. . robust probabilistic algorithms of fuzzy clustering are considered and investigated for batch processing mode and on-line mode which may be used for clustering in BD bases. Experimental investigations of the considered clusteri
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,Deep Neural Networks and Hybrid GMDH-Neuro-fuzzy Networks in Big Data Analysis, |
Michael Z. Zgurovsky,Yuriy P. Zaychenko |
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Abstract
Deep neural networks (DNN) and their generalizations—hybrid DNN are considered which represent the efficient tools for BD analysis. The properties and drawbacks of deep learning are considered and analyzed. Encoders—decoders and restricted Boltzmann machines are described and their applications for Deep learning implementation are presented. Methods of regularization of DL: penalty functions, Dropout and Bagging are presented. New class of deep learning networks are suggested and presented. so-called GMDH-neo-fuzzy networks representing a combination of self-organization method GMDH and fuzzy neural networks. Due to principle of self-organization and small number of tuning parameters GMDH enables to simplify and accelerate the training of DN. Several variants of this class hybrid networks are considered and algorithms of their structure synthesis based on GMDH are suggested and analyzed. The application of GMDH enables to reduce dimensionality of training DN and accelerate the convergence of training and by this solve some problems of Big Data Analysis. Experimental investigations of hybrid GMDH-neo-fuzzy networks are carried out and their results are presented and analyzed.
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,Pattern Recognition in Big Data Analysis, |
Michael Z. Zgurovsky,Yuriy P. Zaychenko |
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Abstract
In this chapter problems and methods of pattern recognition are considered and analyzed. Fuzzy neural network NEF Class is presented and algorithms of its training are considered. Unlike conventional neural networks (NN) FNN NEFClass may work with uncertain and fuzzy input data and additionally it may use expert knowledge in the form of fuzzy rule base. The training algorithms of FNN NEFClass have accelerated convergence in comparison to conventional NN which make it efficient tools for some classification problems with BD. The applications of NEFClass for pattern recognition of optical images and classification of medical images of human organ tissue in medical diagnostics problems are presented and their experimental results are analyzed. The hybrid fuzzy CNN network for medical images of breast cancer classification is suggested in which CNN VGG-16 is used for informative features extraction while FNN NEF Class is used as classifier of breast tumors. The experimental investigations of hybrid CNN network are presented and comparison with conventional CNN was performed. The problem of features dimensionality reduction in classification problem is considered and for its solution PC
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,Intellectual Analysis of Systemic World Conflicts and Global Forecast for the 21st Century, |
Michael Z. Zgurovsky,Yuriy P. Zaychenko |
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Abstract
Data on global conflicts took place from 750 B.C. up to now are analyzed and their general pattern is revealed. An attempt is made to foresee the next global conflict called the conflict of the 21st century. Its nature and main characteristics are analyzed. Main global threats are listed, and their impact on five groups of countries is determined using cluster analysis.
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