Big data: conceptual analysis and applications

✍ Scribed by Zgurovsky M.Z., Zaychenko Y.P

• Publisher: Springer International Publishing
• Year: 2020
• Tongue: English
• Leaves: 298
• Series: Studies in Big Data
• Edition: 1st edition 2020
• Category: Library

✦ Table of Contents

Preface......Page 6
Contents......Page 8
Introduction......Page 13
Networking Ways of Communication Between People on the Planet......Page 15
Information Technology......Page 16
Science......Page 17
Technology......Page 18
1.1 Introduction......Page 24
1.2 Cluster Analysis, Problem Definition. Criteria of Quality and Metrics......Page 25
1.3 Classification of Algorithms of Cluster Analysis......Page 27
1.3.2 Divisional Algorithms......Page 28
1.3.3 Not Hierarchical Algorithms......Page 30
1.4.1 Algorithm of Fuzzy C-Means......Page 32
1.4.2 Definition of Initial Location of the Centers of Clusters......Page 34
1.5 Gustavson-Kessel’s Fuzzy Cluster Analysis Algorithm......Page 35
1.5.1 Description of Gustavson-Kessel Algorithm......Page 36
1.6.1 Possibilistic Clustering Algorithm......Page 37
1.6.2 Recurrent Fuzzy Clustering Algorithms......Page 38
1.6.3 Robust Adaptive Algorithms of Probabilistic Fuzzy Clustering......Page 39
1.7 Robust Recursive Algorithm of Possibilistic Fuzzy Clustering for Big Data......Page 42
1.8 Application of Fuzzy Clustering Methods in the Problems of Automatic Classification......Page 45
1.9 Conclusions......Page 64
References......Page 65
2.1 Introduction......Page 66
2.2 Autoassociators. Autoencoders......Page 67
2.3.1 Energetic Models......Page 70
2.3.2 Restricted Boltzmann Machine (RBM)......Page 71
2.4 Training Method Contrastive Divergence (CD)......Page 73
2.4.1 Training Algorithm Contrastive Divergence (CD-k)......Page 75
2.4.2 Example......Page 76
2.5.2 Stacked RBM......Page 77
2.6.1 Deep Network Pretraining......Page 80
2.7.1 Lp-Regularization of Linear Regression......Page 81
2.7.2 Early Stopping......Page 83
2.7.3 Dropout......Page 85
2.8 Cascade Neo-fuzzy Neural Networks Structure Synthesis and Learning with Application of GMDH......Page 87
2.8.1 The Neo-fuzzy Neuron......Page 88
2.8.2 The Neo-fuzzy Neuron Learning Algorithm......Page 91
2.8.3 The Neo-fuzzy Neural Network and Its Architecture Optimization Using the Group Method of Data Handling......Page 92
2.8.4 The Experimental Investigations of Forecasting with Neo-fuzzy Neural Network......Page 94
2.9 Evolving GMDH-Neuro-fuzzy Network with Small Number of Tuning Parameters......Page 99
2.9.1 Evolving GMDH-Neuro-fuzzy System Architecture......Page 100
2.9.2 Neuro-fuzzy Network with Small Number of Tuning Parameters as a Node of GMDH-System......Page 101
2.9.3 Computational Experiments......Page 104
2.10 A Deep GMDH System Based on the Extended Neo-fuzzy Neuron and Its Training......Page 108
2.10.1 An Architecture of the Deep GMDH Neuro-fuzzy System......Page 110
2.10.1.1 The Extended Neo-fuzzy Neuron......Page 111
2.10.2 The Adjustment Procedures for All Parameters of the System......Page 113
2.10.3 An Experimental Study......Page 115
References......Page 117
3.1 Introduction......Page 119
3.2 FNN NEFClass. Architecture, Properties, the Algorithms of Learning of Base Rules and Membership Functions......Page 120
3.3 Analysis NEFClass Properties. The Modified System NEFClassM......Page 124
3.3.1 The Modified Model NEFCLASS......Page 125
3.4 Experimental Studies. Comparative Analysis of FNN NEFClass and NEFClass-M in Classification Problems......Page 127
3.5 Application of NEFClass in the Problem of Objects Recognition at Electro-Optical Images......Page 128
3.5.1 Gradient Learning Algorithm for NEFClass......Page 129
3.5.2 Genetic Method for Training System NEFClass......Page 131
3.5.3 Experiments on Objects Recognition on Optical Images......Page 132
3.6 Recognition of Images in Medical Diagnostics Using Fuzzy Neural Networks......Page 141
3.6.2 Training of NEFClass System......Page 142
3.6.3 Experimental Investigations......Page 145
3.7.1 State-of-Art Problem Analysis......Page 149
3.7.2 Data Set Description......Page 151
3.7.3 Convolutional Neural Networks Brief Description......Page 153
3.7.5 Experimental Investigations and Results Analysis......Page 156
References......Page 159
4.1 Introduction......Page 162
4.2.1 Fibonacci Pattern of the Emergence of Systemic World Conflicts......Page 163
4.2.2 Conflict of the 21st Century and Analysis of Its Nature......Page 167
4.2.3 Modeling the Total Impact of the Aggregate of 12 Global Threats on Different Countries and Groups of Countries......Page 179
4.3 Interrelation Between Periodic Processes in the Global Economy and Systemic World Conflicts......Page 199
4.3.1 Periodicity of Global Systemic Conflicts and Economic Processes......Page 200
4.3.2 Analysis of the Relationship Between Systemic World Conflicts and the Global Economy......Page 201
4.3.3 Conclusions......Page 207
4.4.1 Initial Definitions......Page 208
4.4.2 Structural Analysis of Global System Conflicts......Page 209
4.4.3 Confirmation of the F-Pattern by Other Independent Studies......Page 212
4.4.4 F-Principle as the Basis of a Metric Study of Global Civilization Processes......Page 214
4.5 Big Solar Spiral of Stirring up Global Systemic Conflicts......Page 215
4.5.1 Synchronous Variation of Solar Activity and Formation of C-Waves of Global Systemic Conflicts......Page 217
4.5.2 Visualization of the Process of “Stirring Up” of the Family of \left{ {{\varvec C}{{\varvec K}} } \right}{{{\varvec K} \in {\varvec I}\left( {1;7} \right)}} -Waves of Global Systemic Conflicts......Page 223
4.5.3 Local “Stirring Up” by {\varvec H}{{\varvec W}}^{{\left( {\varvec K} \right)}} -Ensemble of Schwabe–Wolf Solar Cycles of Evolution Phases of {\varvec C}{{\varvec k}} -Wave of Global Systemic Conflicts......Page 224
4.5.5 Conclusions......Page 226
4.6 Influence of Global Threats on the Sustainable Development of Countries and Regions of the World......Page 231
4.6.1 The Methodology of Sustainable Development Evaluation in Terms of Quality and Security of the Human Life......Page 232
4.6.2 Some Basic Definitions and Concepts......Page 237
4.6.3 Synthesis of Topologies of BBNs......Page 241
4.6.4 Modelling the Influence of Global Threats on the Sustainable Development of Countries and Regions of the World with the Use of BBNs......Page 243
4.6.5 Interpretation of the Generalized Results of Modeling......Page 254
4.6.6 Visualization of Data on Indicators of Sustainable Development for Countries and Regions of the World......Page 255
4.7 The General Concept of the Periodic Systemic World Conflicts......Page 271
4.7.1 Some Concepts and Definitions......Page 274
4.7.2 Geometric Images of C_{{\rm K}}^{{}} -Waves and Ensemble of \left( {SWC} \right){\alpha } -Waves of Systemic World Conflicts......Page 277
4.7.3 Significant Features of SWC-Concept......Page 279
4.7.4 Correlation of Processes of Evolutionary Development of Civilization \varPi{C}^{Ed} and Development of C-Waves of Systemic World Conflicts \pi_{swc}^{es} \left( {{{\cal L}}_{c} \left( {m,n} \right)} \right)......Page 280
4.7.5 The Problem of Identification (Recognition) of C-Waves of Systemic World Conflicts for Big Historical Data......Page 282
4.7.6 Big C -Waves of Systemic World Conflicts......Page 284
4.8 Conclusions......Page 295
References......Page 296

✦ Subjects

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