Data Mining and Machine Learning Applications - Группа авторов

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10.1 Introduction 10.2 Feature Recovering Methodologies 10.3 CPS vs. IT Systems 10.4 Collections, Sources, and Generations of Big Data for CPS 10.5 Spatial Prediction 10.6 Clustering of Big Data 10.7 NoSQL 10.8 Cyber Security and Privacy Big Data 10.9 Smart Grids 10.10 Military Applications 10.11 City Management 10.12 Clinical Applications 10.13 Calamity Events 10.14 Data Streams Clustering by Sensors 10.15 The Flocking Model 10.16 Calculation Depiction 10.17 Initialization 10.18 Representative Maintenance and Clustering 10.19 Results 10.20 Conclusion References

      15  11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining 11.1 Introduction 11.2 Background 11.3 Methodology of CRISP-DM 11.4 Stage One—Determine Business Objectives 11.5 Stage Two—Data Sympathetic 11.6 Stage Three—Data Preparation 11.7 Stage Four—Modeling 11.8 Stage Five—Evaluation 11.9 Stage Six—Deployment 11.10 Data on ERP Systems 11.11 Usage of CRISP-DM Methodology 11.12 Modeling 11.13 Assessment 11.14 Distribution 11.15 Results and Discussion 11.16 Conclusion References

      16  12 Human–Machine Interaction and Visual Data Mining 12.1 Introduction 12.2 Related Researches 12.3 Visual Genes 12.4 Visual Hypotheses 12.5 Visual Strength and Conditioning 12.6 Visual Optimization 12.7 The Vis 09 Model 12.8 Graphic Monitoring and Contact With Human–Computer 12.9 Mining HCI Information Using Inductive Deduction Viewpoint 12.10 Visual Data Mining Methodology 12.11 Machine Learning Algorithms for Hand Gesture Recognition 12.12 Learning 12.13 Detection 12.14 Recognition 12.15 Proposed Methodology for Hand Gesture Recognition 12.16 Result 12.17 Conclusion References

      17  13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection 13.1 Introduction 13.2 Literature Survey 13.3 Methods and Material 13.4 Experimental Results 13.5 Libraries Used 13.6 Comparing Algorithms Based on Decision Boundaries 13.7 Evaluating Results 13.8 Conclusion References

      18  14 New Algorithms and Technologies for Data Mining 14.1 Introduction 14.2 Machine Learning Algorithms 14.3 Supervised Learning 14.4 Unsupervised Learning 14.5 Semi-Supervised Learning 14.6 Regression Algorithms 14.7 Case-Based Algorithms

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