Data Mining and Machine Learning Applications

Description

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning,A review and description of the learning methods in human-computer interaction,Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time,The scope and implementation of a majority of data mining and machine learning strategies.A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly. Cover Half-Title Page Series Page Title Page Copyright Page Contents Preface 1 Introduction to Data Mining 1.1 Introduction 1.1.1 Data Mining 1.2 Knowledge Discovery in Database (KDD) 1.2.1 Importance of Data Mining 1.2.2 Applications of Data Mining 1.2.3 Databases 1.3 Issues in Data Mining 1.4 Data Mining Algorithms 1.5 Data Warehouse 1.6 Data Mining Techniques 1.7 Data Mining Tools 1.7.1 Python for Data Mining 1.7.2 KNIME 1.7.3 Rapid Miner References 2 Classification and Mining Behavior of Data 2.1 Introduction 2.2 Main Characteristics of Mining Behavioral Data 2.2.1 Mining Dynamic/Streaming Data 2.2.2 Mining Graph & Network Data 2.2.3 Mining Heterogeneous/Multi-Source Information 2.2.3.1 Multi-Source and Multidimensional Information 2.2.3.2 Multi-Relational Data 2.2.3.3 Background and Connected Data 2.2.3.4 Complex Data, Sequences, and Events 2.2.3.5 Data Protection and Morals 2.2.4 Mining High Dimensional Data 2.2.5 Mining Imbalanced Data 2.2.5.1 The Class Imbalance Issue 2.2.6 Mining Multimedia Data 2.2.6.1 Common Applications Multimedia Data Mining 2.2.6.2 Multimedia Data Mining Utilizations 2.2.6.3 Multimedia Database Management 2.2.7 Mining Scientific Data 2.2.8 Mining Sequential Data 2.2.9 Mining Social Networks 2.2.9.1 Social-Media Data Mining Reasons 2.2.10 Mining Spatial and Temporal Data 2.2.10.1 Utilizations of Spatial and Temporal Data Mining 2.3 Research Method 2.4 Results 2.5 Discussion 2.6 Conclusion References 3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 3.1 Introduction 3.2 Related Work on Different Recommender System 3.2.1 Challenges in RS 3.2.2 Research Questions and Architecture of This Paper 3.2.3 Background 3.2.4 Analysis 3.2.5 Materials and Methods 3.2.6 Comparative Analysis With Traditional Recommender System 3.2.7 Practical Implications 3.2.8 Conclusion & Future Work References 4 Stream Mining: Introduction, Tools & Techniques and Applications 4.1 Introduction 4.2 Data Reduction: Sampling and Sketching 4.2.1 Sampling 4.2.2 Sketching 4.3 Concept Drift 4.4 Stream Mining Operations 4.4.1 Clustering 4.4.2 Classification 4.4.3 Outlier Detection 4.4.4 Frequent Itemsets Mining 4.5 Tools & Techniques 4.5.1 Implementation in Java 4.5.2 Implementation in Python 4.5.3 Implementation in R 4.6 Applications 4.6.1 Stock Prediction in Share Market 4.6.2 Weather Forecasting System 4.6.3 Finding Trending News and Events 4.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream) 4.6.5 Pollution Control Systems 4.7 Conclusion References 5 Data Mining Tools and Techniques: Clustering Analysis 5.1 Introduction 5.2 Data Mining Task 5.2.1 Data Summarization 5.2.2 Data Clustering 5.2.3 Classification of Data 5.2.4 Data Regression 5.2.5 Data Association 5.3 Data Mining Algorithms and Methodologies 5.3.1 Data Classification Algorithm 5.3.2 Predication 5.3.3 Association Rule 5.3.4 Neural Network 5.3.4.1 Data Clustering Algorithm 5.3.5 In-Depth Study of Gathering Techniques 5.3.6 Data Partitioning Method 5.3.7 Hierarchical Method 5.3.8 Framework-Based Method 5.3.9 Model-Based Method 5.3.10 Thickness-Based Method 5.4 Clustering the Nearest Neighbor 5.4.1 Fuzzy Clustering 5.4.2 K-Algorithm Means 5.5 Data Mining Applications 5.6 Materials and Strategies for Document Clustering 5.6.1 Features Generation 5.7 Discussion and Results 5.7.1 Discussion 5.7.2 Conclusion References 6 Data Mining Implementation Process 6.1 Introduction 6.2 Data Mining Historical Trends 6.3 Processes of Data Analysis 6.3.1 Data Attack 6.3.2 Data Mixing 6.3.3 Data Collection 6.3.4 Data Conversion 6.3.4.1 Data Mining 6.3.4.2 Design Evaluation 6.3.4.3 Data Illustration 6.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process 6.3.5 Business Understanding 6.3.6 Data Understanding 6.3.7 Data Preparation 6.3.8 Modeling 6.3.9 Evaluation 6.3.10 Deployment 6.3.11 Contemporary Developments 6.3.12 An Assortment of Data Mining 6.3.12.1 Using Computational & Connectivity Tools 6.3.12.2 Web Mining 6.3.12.3 Comparative Statement 6.3.13 Advantages of Data Mining 6.3.14 Drawbacks of Data Mining 6.3.15 Data Mining Applications 6.3.16 Methodology 6.3.17 Results 6.3.18 Conclusion and Future Scope References 7 Predictive Analytics in IT Service Management (ITSM) 7.1 Introduction 7.2 Analytics: An Overview 7.2.1 Predictive Analytics 7.3 Significance of Predictive Analytics in ITSM 7.4 Ticket Analytics: A Case Study 7.4.1 Input Parameters 7.4.2 Predictive Modeling 7.4.3 Random Forest Model 7.4.4 Performance of the Predictive Model 7.5 Conclusion References 8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques 8.1 Introduction 8.2 Literature Review 8.3 Methodology and Implementation 8.3.1 Selection of the Independent Variables 8.4 Data Partitioning 8.4.1 Interpreting the Results of Logistic Regression Model 8.5 Conclusions References 9 Inductive Learning Including Decision Tree and Rule Induction Learning 9.1 Introduction 9.2 The Inductive Learning Algorithm (ILA) 9.3 Proposed Algorithms 9.4 Divide & Conquer Algorithm 9.4.1 Decision Tree 9.5 Decision Tree Algorithms 9.5.1 ID3 Algorithm 9.5.2 Separate and Conquer Algorithm 9.5.3 RULE EXTRACTOR-1 9.5.4 Inductive Learning Applications 9.5.4.1 Education 9.5.4.2 Making Credit Decisions 9.5.5 Multidimensional Databases and OLAP 9.5.6 Fuzzy Choice Trees 9.5.7 Fuzzy Choice Tree Development From a Multidimensional Database 9.5.8 Execution and Results 9.6 Conclusion and Future Work References 10 Data Mining for Cyber-Physical Systems 10.1 Introduction 10.1.1 Models of Cyber-Physical System 10.1.2 Statistical Model-Based Methodologies 10.1.3 Spatial-and-Transient Closeness-Based Methodologies 10.2 Feature Recovering Methodologies 10.3 CPS vs. IT Systems 10.4 Collections, Sources, and Generations of Big Data for CPS 10.4.1 Establishing Conscious Computation and Information Systems 10.5 Spatial Prediction 10.5.1 Global Optimization 10.5.2 Big Data Analysis CPS 10.5.3 Analysis of Cloud Data 10.5.4 Analysis of Multi-Cloud Data 10.6 Clustering of Big Data 10.7 NoSQL 10.8 Cyber Security and Privacy Big Data 10.8.1 Protection of Big Computing and Storage 10.8.2 Big Data Analytics Protection 10.8.3 Big Data CPS Applications 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 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 11.4.1 What Are the Ideal Yields of the Venture? 11.4.2 Evaluate the Current Circumstance 11.4.3 Realizes Data Mining Goals 11.5 Stage Two—Data Sympathetic 11.5.1 Portray Data 11.5.2 Investigate Facts 11.5.3 Confirm Data Quality 11.5.4 Data Excellence Description 11.6 Stage Three—Data Preparation 11.6.1 Select Your Data 11.6.2 The Data Is Processed 11.6.3 Data Needed to Build 11.6.4 Combine Information 11.7 Stage Four—Modeling 11.7.1 Select Displaying Strategy 11.7.2 Produce an Investigation Plan 11.7.3 Fabricate Ideal 11.7.4 Evaluation Model 11.8 Stage Five—Evaluation 11.8.1 Assess Your Outcomes 11.8.2 Survey Measure 11.8.3 Decide on the Subsequent Stages 11.9 Stage Six—Deployment 11.9.1 Plan Arrangement 11.9.2 Plan Observing and Support 11.9.3 Produce the Last Report 11.9.4 Audit Venture 11.10 Data on ERP Systems 11.11 Usage of CRISP-DM Methodology 11.12 Modeling 11.12.1 Association Rule Mining (ARM) or Association Analysis 11.12.2 Classification Algorithms 11.12.3 Regression Algorithms 11.12.4 Clustering Algorithms 11.13 Assessment 11.14 Distribution 11.15 Results and Discussion 11.16 Conclusion References 12 Human–Machine Interaction and Visual Data Mining 12.1 Introduction 12.2 Related Researches 12.2.1 Data Mining 12.2.2 Data Visualization 12.2.3 Visual Learning 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 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 13.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm 13.4 Experimental Results 13.5 Libraries Used 13.6 Comparing Algorithms Based on Decision Boundaries 13.7 Evaluating Results 13.8 Conclusion References 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 14.8 Regularization Algorithms 14.9 Decision Tree Algorithms 14.10 Bayesian Algorithms 14.11 Clustering Algorithms 14.12 Association Rule Learning Algorithms 14.13 Artificial Neural Network Algorithms 14.14 Deep Learning Algorithms 14.15 Dimensionality Reduction Algorithms 14.16 Ensemble Algorithms 14.17 Other Machine Learning Algorithms 14.18 Data Mining Assignments 14.19 Data Mining Models 14.20 Non-Parametric & Parametric Models 14.21 Flexible vs. Restrictive Methods 14.22 Unsupervised vs. Supervised Learning 14.23 Data Mining Methods 14.24 Proposed Algorithm 14.24.1 Organization Formation Procedure 14.25 The Regret of Learning Phase 14.26 Conclusion References 15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier 15.1 Introduction 15.2 Related Work 15.3 Material and Methods 15.3.1 Dataset Description 15.3.2 Proposed Methodology 15.3.3 Normalization 15.3.4 Preprocessing Using PCA 15.3.5 Restricted Boltzmann Machine (RBM) 15.3.6 Stochastic Binary Units (Bernoulli Variables) 15.3.7 Training 15.4 Experimental Framework 15.5 Experimental Results and Discussion 15.5.1 Performance Measurement Criteria 15.5.2 Experimental Results 15.6 Discussion 15.7 Conclusion References 16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques 16.1 Introduction 16.2 Related Work 16.2.1 WoSApp 16.2.2 Abhaya 16.2.3 Women Empowerment 16.2.4 Nirbhaya 16.2.5 Glympse 16.2.6 Fightback 16.2.7 Versatile-Based 16.2.8 RFID 16.2.9 Self-Preservation Framework for Women With Area Following and SMS Alarming Through GSM Network 16.2.10 Safe: A Women Security Framework 16.2.11 Intelligent Safety System For Women Security 16.2.12 A Mobile-Based Women Safety Application 16.2.13 Self-Salvation—The Women’s Security Module 16.3 Issue and Solution 16.3.1 Inspiration 16.3.2 Issue Statement and Choice of Solution 16.4 Selection of Data 16.5 Pre-Preparation Data 16.5.1 Simulation 16.5.2 Assessment 16.5.3 Forecast 16.6 Application Development 16.6.1 Methodology 16.6.2 AI Model 16.6.3 Innovations Used The Proposed Application Has Utilized After Technologies 16.7 Use Case For The Application 16.7.1 Application Icon 16.7.2 Enlistment Form 16.7.3 Login Form 16.7.4 Misconduct Place Detector 16.7.5 Help Button 16.8 Conclusion References 17 Conclusion and Future Direction in Data Mining and Machine Learning 17.1 Introduction 17.2 Machine Learning 17.2.1 Neural Network 17.2.2 Deep Learning 17.2.3 Three Activities for Object Recognition 17.3 Conclusion References Index