Rohit Raja, PhD is an associate professor in the IT Department, Guru Ghasidas Vishwavidyalaya, Bilaspur (CG), India. He has published more than 80 research papers in peer-reviewed journals as well as 9 patents. Kapil Kumar Nagwanshi, PhD is an associate professor at Mukesh Patel School of Technology Management & Engineering, Shirpur Campus, SVKM’s Narsee Monjee Institute of Management Studies Mumbai, India. Sandeep Kumar, PhD is a professor in the Department of Electronics & Communication Engineering, Sreyas Institute of Engineering & Technology, Hyderabad, India. His area of research includes embedded systems, image processing, and biometrics. He has published more than 60 research papers in peer-reviewed journals as well as 6 patents. K. Ramya Laxmi, PhD is an associate professor in the CSE Department at the Sreyas Institute of Engineering and Technology, Hyderabad. Her research interest covers the fields of data mining and image processing.

• Preface xvii1 Introduction to Data Mining 1Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Sandeep Kumar1.1. Introduction 11.1.1 Data Mining 11.2 Knowledge Discovery in Database (KDD) 21.2.1 Importance of Data Mining 31.2.2 Applications of Data Mining 31.2.3 Databases 41.3 Issues in Data Mining 61.4 Data Mining Algorithms 71.5 Data Warehouse 91.6 Data Mining Techniques 101.7 Data Mining Tools 111.7.1 Python for Data Mining 121.7.2 KNIME 131.7.3 Rapid Miner 17References 182 Classification and Mining Behavior of Data 21Srinivas Konda, Kavitarani Balmuri and Kishore Kumar Mamidala2.1 Introduction 222.2 Main Characteristics of Mining Behavioral Data 232.2.1 Mining Dynamic/Streaming Data 232.2.2 Mining Graph & Network Data 242.2.3 Mining Heterogeneous/Multi-Source Information 252.2.3.1 Multi-Source and Multidimensional Information 262.2.3.2 Multi-Relational Data 262.2.3.3 Background and Connected Data 272.2.3.4 Complex Data, Sequences, and Events 272.2.3.5 Data Protection and Morals 272.2.4 Mining High Dimensional Data 282.2.5 Mining Imbalanced Data 292.2.5.1 The Class Imbalance Issue 292.2.6 Mining Multimedia Data 302.2.6.1 Common Applications Multimedia Data Mining 312.2.6.2 Multimedia Data Mining Utilizations 312.2.6.3 Multimedia Database Management 322.2.7 Mining Scientific Data 342.2.8 Mining Sequential Data 352.2.9 Mining Social Networks 362.2.9.1 Social-Media Data Mining Reasons 392.2.10 Mining Spatial and Temporal Data 402.2.10.1 Utilizations of Spatial and Temporal Data Mining 412.3 Research Method 442.4 Results 482.5 Discussion 492.6 Conclusion 50References 513 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 57Rakhi Seth and Aakanksha Sharaff3.1 Introduction 583.2 Related Work on Different Recommender System 603.2.1 Challenges in RS 653.2.2 Research Questions and Architecture of This Paper 663.2.3 Background 683.2.3.1 The Architecture of Hybrid Approach 693.2.4 Analysis 783.2.4.1 Evaluation Measures 783.2.5 Materials and Methods 813.2.6 Comparative Analysis With Traditional Recommender System 853.2.7 Practical Implications 853.2.8 Conclusion & Future Work 94References 944 Stream Mining: Introduction, Tools & Techniques and Applications 99Naresh Kumar Nagwani4.1 Introduction 1004.2 Data Reduction: Sampling and Sketching 1014.2.1 Sampling 1014.2.2 Sketching 1024.3 Concept Drift 1034.4 Stream Mining Operations 1054.4.1 Clustering 1054.4.2 Classification 1064.4.3 Outlier Detection 1074.4.4 Frequent Itemsets Mining 1084.5 Tools & Techniques 1094.5.1 Implementation in Java 1104.5.2 Implementation in Python 1164.5.3 Implementation in R 1184.6 Applications 1204.6.1 Stock Prediction in Share Market 1204.6.2 Weather Forecasting System 1214.6.3 Finding Trending News and Events 1214.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream) 1214.6.5 Pollution Control Systems 1224.7 Conclusion 122References 1225 Data Mining Tools and Techniques: Clustering Analysis 125Rohit Miri, Amit Kumar Dewangan, S.R. Tandan, Priya Bhatnagar and Hiral Raja5.1 Introduction 1265.2 Data Mining Task 1295.2.1 Data Summarization 1295.2.2 Data Clustering 1295.2.3 Classification of Data 1295.2.4 Data Regression 1305.2.5 Data Association 1305.3 Data Mining Algorithms and Methodologies 1315.3.1 Data Classification Algorithm 1315.3.2 Predication 1325.3.3 Association Rule 1325.3.4 Neural Network 1325.3.4.1 Data Clustering Algorithm 1335.3.5 In-Depth Study of Gathering Techniques 1345.3.6 Data Partitioning Method 1345.3.7 Hierarchical Method 1345.3.8 Framework-Based Method 1365.3.9 Model-Based Method 1365.3.10 Thickness-Based Method 1365.4 Clustering the Nearest Neighbor 1365.4.1 Fuzzy Clustering 1375.4.2 K-Algorithm Means 1375.5 Data Mining Applications 1385.6 Materials and Strategies for Document Clustering 1405.6.1 Features Generation 1425.7 Discussion and Results 1435.7.1 Discussion 1465.7.2 Conclusion 149References 1496 Data Mining Implementation Process 151Kamal K. Mehta, Rajesh Tiwari and Nishant Behar6.1 Introduction 1516.2 Data Mining Historical Trends 1526.3 Processes of Data Analysis 1536.3.1 Data Attack 1536.3.2 Data Mixing 1536.3.3 Data Collection 1536.3.4 Data Conversion 1546.3.4.1 Data Mining 1546.3.4.2 Design Evaluation 1546.3.4.3 Data Illustration 1546.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process 1546.3.5 Business Understanding 1556.3.6 Data Understanding 1566.3.7 Data Preparation 1586.3.8 Modeling 1596.3.9 Evaluation 1606.3.10 Deployment 1616.3.11 Contemporary Developments 1626.3.12 An Assortment of Data Mining 1626.3.12.1 Using Computational & Connectivity Tools 1636.3.12.2 Web Mining 1636.3.12.3 Comparative Statement 1636.3.13 Advantages of Data Mining 1636.3.14 Drawbacks of Data Mining 1656.3.15 Data Mining Applications 1656.3.16 Methodology 1676.3.17 Results 1696.3.18 Conclusion and Future Scope 171References 1727 Predictive Analytics in IT Service Management (ITSM) 175Sharon Christa I.L. and Suma V.7.1 Introduction 1767.2 Analytics: An Overview 1787.2.1 Predictive Analytics 1807.3 Significance of Predictive Analytics in ITSM 1817.4 Ticket Analytics: A Case Study 1867.4.1 Input Parameters 1887.4.2 Predictive Modeling 1887.4.3 Random Forest Model 1897.4.4 Performance of the Predictive Model 1917.5 Conclusion 191References 1928 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques 195K. Ramya Laxmi, Sumit Srivastava, K. Madhuravani, S. Pallavi and Omprakash Dewangan8.1 Introduction 1968.2 Literature Review 1988.3 Methodology and Implementation 2008.3.1 Selection of the Independent Variables 2008.4 Data Partitioning 2038.4.1 Interpreting the Results of Logistic Regression Model 2038.5 Conclusions 204References 2059 Inductive Learning Including Decision Tree and Rule Induction Learning 209Raj Kumar Patra, A. Mahendar and G. Madhukar9.1 Introduction 2109.2 The Inductive Learning Algorithm (ILA) 2129.3 Proposed Algorithms 2139.4 Divide & Conquer Algorithm 2149.4.1 Decision Tree 2149.5 Decision Tree Algorithms 2159.5.1 ID3 Algorithm 2159.5.2 Separate and Conquer Algorithm 2179.5.3 RULE EXTRACTOR-1 2269.5.4 Inductive Learning Applications 2269.5.4.1 Education 2269.5.4.2 Making Credit Decisions 2279.5.5 Multidimensional Databases and OLAP 2289.5.6 Fuzzy Choice Trees 2289.5.7 Fuzzy Choice Tree Development From a Multidimensional Database 2299.5.8 Execution and Results 2309.6 Conclusion and Future Work 231References 23210 Data Mining for Cyber-Physical Systems 235M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi and Shaik Munawar10.1 Introduction 23610.1.1 Models of Cyber-Physical System 23810.1.2 Statistical Model-Based Methodologies 23910.1.3 Spatial-and-Transient Closeness-Based Methodologies 24010.2 Feature Recovering Methodologies 24010.3 CPS vs. IT Systems 24110.4 Collections, Sources, and Generations of Big Data for CPS 24210.4.1 Establishing Conscious Computation and Information Systems 24310.5 Spatial Prediction 24310.5.1 Global Optimization 24410.5.2 Big Data Analysis CPS 24510.5.3 Analysis of Cloud Data 24510.5.4 Analysis of Multi-Cloud Data 24710.6 Clustering of Big Data 24810.7 NoSQL 25110.8 Cyber Security and Privacy Big Data 25110.8.1 Protection of Big Computing and Storage 25210.8.2 Big Data Analytics Protection 25210.8.3 Big Data CPS Applications 25610.9 Smart Grids 25610.10 Military Applications 25810.11 City Management 25910.12 Clinical Applications 26110.13 Calamity Events 26210.14 Data Streams Clustering by Sensors 26310.15 The Flocking Model 26310.16 Calculation Depiction 26410.17 Initialization 26510.18 Representative Maintenance and Clustering 26610.19 Results 26710.20 Conclusion 268References 26911 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining 281Vivek Parganiha, Soorya Prakash Shukla and Lokesh Kumar Sharma11.1 Introduction 28211.2 Background 28311.3 Methodology of CRISP-DM 28411.4 Stage One—Determine Business Objectives 28611.4.1 What Are the Ideal Yields of the Venture? 28711.4.2 Evaluate the Current Circumstance 28811.4.3 Realizes Data Mining Goals 28911.5 Stage Two—Data Sympathetic 29011.5.1 Portray Data 29111.5.2 Investigate Facts 29111.5.3 Confirm Data Quality 29211.5.4 Data Excellence Description 29211.6 Stage Three—Data Preparation 29211.6.1 Select Your Data 29411.6.2 The Data Is Processed 29411.6.3 Data Needed to Build 29411.6.4 Combine Information 29511.7 Stage Four—Modeling 29511.7.1 Select Displaying Strategy 29611.7.2 Produce an Investigation Plan 29711.7.3 Fabricate Ideal 29711.7.4 Evaluation Model 29711.8 Stage Five—Evaluation 29811.8.1 Assess Your Outcomes 29911.8.2 Survey Measure 29911.8.3 Decide on the Subsequent Stages 30011.9 Stage Six—Deployment 30011.9.1 Plan Arrangement 30111.9.2 Plan Observing and Support 30111.9.3 Produce the Last Report 30211.9.4 Audit Venture 30211.10 Data on ERP Systems 30211.11 Usage of CRISP-DM Methodology 30411.12 Modeling 30611.12.1 Association Rule Mining (ARM) or Association Analysis 30711.12.2 Classification Algorithms 30711.12.3 Regression Algorithms 30811.12.4 Clustering Algorithms 30811.13 Assessment 31011.14 Distribution 31011.15 Results and Discussion 31011.16 Conclusion 311References 31412 Human–Machine Interaction and Visual Data Mining 317Upasana Sinha, Akanksha Gupta, Samera Khan, Shilpa Rani and Swati Jain12.1 Introduction 31812.2 Related Researches 32012.2.1 Data Mining 32312.2.2 Data Visualization 32312.2.3 Visual Learning 32412.3 Visual Genes 32512.4 Visual Hypotheses 32612.5 Visual Strength and Conditioning 32612.6 Visual Optimization 32712.7 The Vis 09 Model 32712.8 Graphic Monitoring and Contact With Human–Computer 32812.9 Mining HCI Information Using Inductive Deduction Viewpoint 33212.10 Visual Data Mining Methodology 33412.11 Machine Learning Algorithms for Hand Gesture Recognition 33812.12 Learning 33812.13 Detection 33912.14 Recognition 34012.15 Proposed Methodology for Hand Gesture Recognition 34012.16 Result 34312.17 Conclusion 343References 34413 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection 349Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu13.1 Introduction 34913.2 Literature Survey 35213.3 Methods and Material 35313.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm 35513.4 Experimental Results 35713.5 Libraries Used 35713.6 Comparing Algorithms Based on Decision Boundaries 35713.7 Evaluating Results 35813.8 Conclusion 361References 36114 New Algorithms and Technologies for Data Mining 365Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla and Maguluri Sudeep Joel14.1 Introduction 36614.2 Machine Learning Algorithms 36814.3 Supervised Learning 36814.4 Unsupervised Learning 36914.5 Semi-Supervised Learning 36914.6 Regression Algorithms 37114.7 Case-Based Algorithms 37114.8 Regularization Algorithms 37214.9 Decision Tree Algorithms 37214.10 Bayesian Algorithms 37314.11 Clustering Algorithms 37414.12 Association Rule Learning Algorithms 37514.13 Artificial Neural Network Algorithms 37514.14 Deep Learning Algorithms 37614.15 Dimensionality Reduction Algorithms 37714.16 Ensemble Algorithms 37714.17 Other Machine Learning Algorithms 37814.18 Data Mining Assignments 37814.19 Data Mining Models 38114.20 Non-Parametric & Parametric Models 38114.21 Flexible vs. Restrictive Methods 38214.22 Unsupervised vs. Supervised Learning 38214.23 Data Mining Methods 38414.24 Proposed Algorithm 38714.24.1 Organization Formation Procedure 38714.25 The Regret of Learning Phase 38814.26 Conclusion 392References 39215 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier 397Sudesh Kumar, Rekh Ram Janghel and Satya Prakash Sahu15.1 Introduction 39815.2 Related Work 40015.3 Material and Methods 40115.3.1 Dataset Description 40115.3.2 Proposed Methodology 40315.3.3 Normalization 40415.3.4 Preprocessing Using PCA 40415.3.5 Restricted Boltzmann Machine (RBM) 40615.3.6 Stochastic Binary Units (Bernoulli Variables) 40715.3.7 Training 40815.3.7.1 Gibbs Sampling 40915.3.7.2 Contrastive Divergence (CD) 40915.4 Experimental Framework 41015.5 Experimental Results and Discussion 41215.5.1 Performance Measurement Criteria 41215.5.2 Experimental Results 41215.6 Discussion 41415.7 Conclusion 418References 41916 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques 423Nanda R. Wagh and Sanjay R. Sutar16.1 Introduction 42416.2 Related Work 42416.2.1 WoSApp 42416.2.2 Abhaya 42516.2.3 Women Empowerment 42516.2.4 Nirbhaya 42516.2.5 Glympse 42616.2.6 Fightback 42616.2.7 Versatile-Based 42616.2.8 RFID 42616.2.9 Self-Preservation Framework for WomenBWith Area Following and SMS Alarming Through GSM Network 42616.2.10 Safe: A Women Security Framework 42716.2.11 Intelligent Safety System For Women Security 42716.2.12 A Mobile-Based Women Safety Application 42716.2.13 Self-Salvation—The Women’s Security Module 42716.3 Issue and Solution 42716.3.1 Inspiration 42716.3.2 Issue Statement and Choice of Solution 42816.4 Selection of Data 42816.5 Pre-Preparation Data 43016.5.1 Simulation 43116.5.2 Assessment 43116.5.3 Forecast 43416.6 Application Development 43616.6.1 Methodology 43616.6.2 AI Model 43716.6.3 Innovations Used The Proposed Application Has Utilized After Technologies 43716.7 Use Case For The Application 43716.7.1 Application Icon 43716.7.2 Enlistment Form 43816.7.3 Login Form 43916.7.4 Misconduct Place Detector 43916.7.5 Help Button 44016.8 Conclusion 443References 44317 Conclusion and Future Direction in Data Mining and Machine Learning 447Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Ramakant Chandrakar17.1 Introduction 44817.2 Machine Learning 45117.2.1 Neural Network 45217.2.2 Deep Learning 45217.2.3 Three Activities for Object Recognition 45317.3 Conclusion 457References 457Index 461

Rohit Raja, PhD is an associate professor in the IT Department, Guru Ghasidas Vishwavidyalaya, Bilaspur (CG), India. He has published more than 80 research papers in peer-reviewed journals as well as 9 patents. Kapil Kumar Nagwanshi, PhD is an associate professor at Mukesh Patel School of Technology Management & Engineering, Shirpur Campus, SVKM’s Narsee Monjee Institute of Management Studies Mumbai, India. Sandeep Kumar, PhD is a professor in the Department of Electronics & Communication Engineering, Sreyas Institute of Engineering & Technology, Hyderabad, India. His area of research includes embedded systems, image processing, and biometrics. He has published more than 60 research papers in peer-reviewed journals as well as 6 patents. K. Ramya Laxmi, PhD is an associate professor in the CSE Department at the Sreyas Institute of Engineering and Technology, Hyderabad. Her research interest covers the fields of data mining and image processing.

• Preface xvii1 Introduction to Data Mining 1Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Sandeep Kumar1.1. Introduction 11.1.1 Data Mining 11.2 Knowledge Discovery in Database (KDD) 21.2.1 Importance of Data Mining 31.2.2 Applications of Data Mining 31.2.3 Databases 41.3 Issues in Data Mining 61.4 Data Mining Algorithms 71.5 Data Warehouse 91.6 Data Mining Techniques 101.7 Data Mining Tools 111.7.1 Python for Data Mining 121.7.2 KNIME 131.7.3 Rapid Miner 17References 182 Classification and Mining Behavior of Data 21Srinivas Konda, Kavitarani Balmuri and Kishore Kumar Mamidala2.1 Introduction 222.2 Main Characteristics of Mining Behavioral Data 232.2.1 Mining Dynamic/Streaming Data 232.2.2 Mining Graph & Network Data 242.2.3 Mining Heterogeneous/Multi-Source Information 252.2.3.1 Multi-Source and Multidimensional Information 262.2.3.2 Multi-Relational Data 262.2.3.3 Background and Connected Data 272.2.3.4 Complex Data, Sequences, and Events 272.2.3.5 Data Protection and Morals 272.2.4 Mining High Dimensional Data 282.2.5 Mining Imbalanced Data 292.2.5.1 The Class Imbalance Issue 292.2.6 Mining Multimedia Data 302.2.6.1 Common Applications Multimedia Data Mining 312.2.6.2 Multimedia Data Mining Utilizations 312.2.6.3 Multimedia Database Management 322.2.7 Mining Scientific Data 342.2.8 Mining Sequential Data 352.2.9 Mining Social Networks 362.2.9.1 Social-Media Data Mining Reasons 392.2.10 Mining Spatial and Temporal Data 402.2.10.1 Utilizations of Spatial and Temporal Data Mining 412.3 Research Method 442.4 Results 482.5 Discussion 492.6 Conclusion 50References 513 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 57Rakhi Seth and Aakanksha Sharaff3.1 Introduction 583.2 Related Work on Different Recommender System 603.2.1 Challenges in RS 653.2.2 Research Questions and Architecture of This Paper 663.2.3 Background 683.2.3.1 The Architecture of Hybrid Approach 693.2.4 Analysis 783.2.4.1 Evaluation Measures 783.2.5 Materials and Methods 813.2.6 Comparative Analysis With Traditional Recommender System 853.2.7 Practical Implications 853.2.8 Conclusion & Future Work 94References 944 Stream Mining: Introduction, Tools & Techniques and Applications 99Naresh Kumar Nagwani4.1 Introduction 1004.2 Data Reduction: Sampling and Sketching 1014.2.1 Sampling 1014.2.2 Sketching 1024.3 Concept Drift 1034.4 Stream Mining Operations 1054.4.1 Clustering 1054.4.2 Classification 1064.4.3 Outlier Detection 1074.4.4 Frequent Itemsets Mining 1084.5 Tools & Techniques 1094.5.1 Implementation in Java 1104.5.2 Implementation in Python 1164.5.3 Implementation in R 1184.6 Applications 1204.6.1 Stock Prediction in Share Market 1204.6.2 Weather Forecasting System 1214.6.3 Finding Trending News and Events 1214.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream) 1214.6.5 Pollution Control Systems 1224.7 Conclusion 122References 1225 Data Mining Tools and Techniques: Clustering Analysis 125Rohit Miri, Amit Kumar Dewangan, S.R. Tandan, Priya Bhatnagar and Hiral Raja5.1 Introduction 1265.2 Data Mining Task 1295.2.1 Data Summarization 1295.2.2 Data Clustering 1295.2.3 Classification of Data 1295.2.4 Data Regression 1305.2.5 Data Association 1305.3 Data Mining Algorithms and Methodologies 1315.3.1 Data Classification Algorithm 1315.3.2 Predication 1325.3.3 Association Rule 1325.3.4 Neural Network 1325.3.4.1 Data Clustering Algorithm 1335.3.5 In-Depth Study of Gathering Techniques 1345.3.6 Data Partitioning Method 1345.3.7 Hierarchical Method 1345.3.8 Framework-Based Method 1365.3.9 Model-Based Method 1365.3.10 Thickness-Based Method 1365.4 Clustering the Nearest Neighbor 1365.4.1 Fuzzy Clustering 1375.4.2 K-Algorithm Means 1375.5 Data Mining Applications 1385.6 Materials and Strategies for Document Clustering 1405.6.1 Features Generation 1425.7 Discussion and Results 1435.7.1 Discussion 1465.7.2 Conclusion 149References 1496 Data Mining Implementation Process 151Kamal K. Mehta, Rajesh Tiwari and Nishant Behar6.1 Introduction 1516.2 Data Mining Historical Trends 1526.3 Processes of Data Analysis 1536.3.1 Data Attack 1536.3.2 Data Mixing 1536.3.3 Data Collection 1536.3.4 Data Conversion 1546.3.4.1 Data Mining 1546.3.4.2 Design Evaluation 1546.3.4.3 Data Illustration 1546.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process 1546.3.5 Business Understanding 1556.3.6 Data Understanding 1566.3.7 Data Preparation 1586.3.8 Modeling 1596.3.9 Evaluation 1606.3.10 Deployment 1616.3.11 Contemporary Developments 1626.3.12 An Assortment of Data Mining 1626.3.12.1 Using Computational & Connectivity Tools 1636.3.12.2 Web Mining 1636.3.12.3 Comparative Statement 1636.3.13 Advantages of Data Mining 1636.3.14 Drawbacks of Data Mining 1656.3.15 Data Mining Applications 1656.3.16 Methodology 1676.3.17 Results 1696.3.18 Conclusion and Future Scope 171References 1727 Predictive Analytics in IT Service Management (ITSM) 175Sharon Christa I.L. and Suma V.7.1 Introduction 1767.2 Analytics: An Overview 1787.2.1 Predictive Analytics 1807.3 Significance of Predictive Analytics in ITSM 1817.4 Ticket Analytics: A Case Study 1867.4.1 Input Parameters 1887.4.2 Predictive Modeling 1887.4.3 Random Forest Model 1897.4.4 Performance of the Predictive Model 1917.5 Conclusion 191References 1928 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques 195K. Ramya Laxmi, Sumit Srivastava, K. Madhuravani, S. Pallavi and Omprakash Dewangan8.1 Introduction 1968.2 Literature Review 1988.3 Methodology and Implementation 2008.3.1 Selection of the Independent Variables 2008.4 Data Partitioning 2038.4.1 Interpreting the Results of Logistic Regression Model 2038.5 Conclusions 204References 2059 Inductive Learning Including Decision Tree and Rule Induction Learning 209Raj Kumar Patra, A. Mahendar and G. Madhukar9.1 Introduction 2109.2 The Inductive Learning Algorithm (ILA) 2129.3 Proposed Algorithms 2139.4 Divide & Conquer Algorithm 2149.4.1 Decision Tree 2149.5 Decision Tree Algorithms 2159.5.1 ID3 Algorithm 2159.5.2 Separate and Conquer Algorithm 2179.5.3 RULE EXTRACTOR-1 2269.5.4 Inductive Learning Applications 2269.5.4.1 Education 2269.5.4.2 Making Credit Decisions 2279.5.5 Multidimensional Databases and OLAP 2289.5.6 Fuzzy Choice Trees 2289.5.7 Fuzzy Choice Tree Development From a Multidimensional Database 2299.5.8 Execution and Results 2309.6 Conclusion and Future Work 231References 23210 Data Mining for Cyber-Physical Systems 235M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi and Shaik Munawar10.1 Introduction 23610.1.1 Models of Cyber-Physical System 23810.1.2 Statistical Model-Based Methodologies 23910.1.3 Spatial-and-Transient Closeness-Based Methodologies 24010.2 Feature Recovering Methodologies 24010.3 CPS vs. IT Systems 24110.4 Collections, Sources, and Generations of Big Data for CPS 24210.4.1 Establishing Conscious Computation and Information Systems 24310.5 Spatial Prediction 24310.5.1 Global Optimization 24410.5.2 Big Data Analysis CPS 24510.5.3 Analysis of Cloud Data 24510.5.4 Analysis of Multi-Cloud Data 24710.6 Clustering of Big Data 24810.7 NoSQL 25110.8 Cyber Security and Privacy Big Data 25110.8.1 Protection of Big Computing and Storage 25210.8.2 Big Data Analytics Protection 25210.8.3 Big Data CPS Applications 25610.9 Smart Grids 25610.10 Military Applications 25810.11 City Management 25910.12 Clinical Applications 26110.13 Calamity Events 26210.14 Data Streams Clustering by Sensors 26310.15 The Flocking Model 26310.16 Calculation Depiction 26410.17 Initialization 26510.18 Representative Maintenance and Clustering 26610.19 Results 26710.20 Conclusion 268References 26911 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining 281Vivek Parganiha, Soorya Prakash Shukla and Lokesh Kumar Sharma11.1 Introduction 28211.2 Background 28311.3 Methodology of CRISP-DM 28411.4 Stage One—Determine Business Objectives 28611.4.1 What Are the Ideal Yields of the Venture? 28711.4.2 Evaluate the Current Circumstance 28811.4.3 Realizes Data Mining Goals 28911.5 Stage Two—Data Sympathetic 29011.5.1 Portray Data 29111.5.2 Investigate Facts 29111.5.3 Confirm Data Quality 29211.5.4 Data Excellence Description 29211.6 Stage Three—Data Preparation 29211.6.1 Select Your Data 29411.6.2 The Data Is Processed 29411.6.3 Data Needed to Build 29411.6.4 Combine Information 29511.7 Stage Four—Modeling 29511.7.1 Select Displaying Strategy 29611.7.2 Produce an Investigation Plan 29711.7.3 Fabricate Ideal 29711.7.4 Evaluation Model 29711.8 Stage Five—Evaluation 29811.8.1 Assess Your Outcomes 29911.8.2 Survey Measure 29911.8.3 Decide on the Subsequent Stages 30011.9 Stage Six—Deployment 30011.9.1 Plan Arrangement 30111.9.2 Plan Observing and Support 30111.9.3 Produce the Last Report 30211.9.4 Audit Venture 30211.10 Data on ERP Systems 30211.11 Usage of CRISP-DM Methodology 30411.12 Modeling 30611.12.1 Association Rule Mining (ARM) or Association Analysis 30711.12.2 Classification Algorithms 30711.12.3 Regression Algorithms 30811.12.4 Clustering Algorithms 30811.13 Assessment 31011.14 Distribution 31011.15 Results and Discussion 31011.16 Conclusion 311References 31412 Human–Machine Interaction and Visual Data Mining 317Upasana Sinha, Akanksha Gupta, Samera Khan, Shilpa Rani and Swati Jain12.1 Introduction 31812.2 Related Researches 32012.2.1 Data Mining 32312.2.2 Data Visualization 32312.2.3 Visual Learning 32412.3 Visual Genes 32512.4 Visual Hypotheses 32612.5 Visual Strength and Conditioning 32612.6 Visual Optimization 32712.7 The Vis 09 Model 32712.8 Graphic Monitoring and Contact With Human–Computer 32812.9 Mining HCI Information Using Inductive Deduction Viewpoint 33212.10 Visual Data Mining Methodology 33412.11 Machine Learning Algorithms for Hand Gesture Recognition 33812.12 Learning 33812.13 Detection 33912.14 Recognition 34012.15 Proposed Methodology for Hand Gesture Recognition 34012.16 Result 34312.17 Conclusion 343References 34413 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection 349Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu13.1 Introduction 34913.2 Literature Survey 35213.3 Methods and Material 35313.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm 35513.4 Experimental Results 35713.5 Libraries Used 35713.6 Comparing Algorithms Based on Decision Boundaries 35713.7 Evaluating Results 35813.8 Conclusion 361References 36114 New Algorithms and Technologies for Data Mining 365Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla and Maguluri Sudeep Joel14.1 Introduction 36614.2 Machine Learning Algorithms 36814.3 Supervised Learning 36814.4 Unsupervised Learning 36914.5 Semi-Supervised Learning 36914.6 Regression Algorithms 37114.7 Case-Based Algorithms 37114.8 Regularization Algorithms 37214.9 Decision Tree Algorithms 37214.10 Bayesian Algorithms 37314.11 Clustering Algorithms 37414.12 Association Rule Learning Algorithms 37514.13 Artificial Neural Network Algorithms 37514.14 Deep Learning Algorithms 37614.15 Dimensionality Reduction Algorithms 37714.16 Ensemble Algorithms 37714.17 Other Machine Learning Algorithms 37814.18 Data Mining Assignments 37814.19 Data Mining Models 38114.20 Non-Parametric & Parametric Models 38114.21 Flexible vs. Restrictive Methods 38214.22 Unsupervised vs. Supervised Learning 38214.23 Data Mining Methods 38414.24 Proposed Algorithm 38714.24.1 Organization Formation Procedure 38714.25 The Regret of Learning Phase 38814.26 Conclusion 392References 39215 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier 397Sudesh Kumar, Rekh Ram Janghel and Satya Prakash Sahu15.1 Introduction 39815.2 Related Work 40015.3 Material and Methods 40115.3.1 Dataset Description 40115.3.2 Proposed Methodology 40315.3.3 Normalization 40415.3.4 Preprocessing Using PCA 40415.3.5 Restricted Boltzmann Machine (RBM) 40615.3.6 Stochastic Binary Units (Bernoulli Variables) 40715.3.7 Training 40815.3.7.1 Gibbs Sampling 40915.3.7.2 Contrastive Divergence (CD) 40915.4 Experimental Framework 41015.5 Experimental Results and Discussion 41215.5.1 Performance Measurement Criteria 41215.5.2 Experimental Results 41215.6 Discussion 41415.7 Conclusion 418References 41916 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques 423Nanda R. Wagh and Sanjay R. Sutar16.1 Introduction 42416.2 Related Work 42416.2.1 WoSApp 42416.2.2 Abhaya 42516.2.3 Women Empowerment 42516.2.4 Nirbhaya 42516.2.5 Glympse 42616.2.6 Fightback 42616.2.7 Versatile-Based 42616.2.8 RFID 42616.2.9 Self-Preservation Framework for WomenBWith Area Following and SMS Alarming Through GSM Network 42616.2.10 Safe: A Women Security Framework 42716.2.11 Intelligent Safety System For Women Security 42716.2.12 A Mobile-Based Women Safety Application 42716.2.13 Self-Salvation—The Women’s Security Module 42716.3 Issue and Solution 42716.3.1 Inspiration 42716.3.2 Issue Statement and Choice of Solution 42816.4 Selection of Data 42816.5 Pre-Preparation Data 43016.5.1 Simulation 43116.5.2 Assessment 43116.5.3 Forecast 43416.6 Application Development 43616.6.1 Methodology 43616.6.2 AI Model 43716.6.3 Innovations Used The Proposed Application Has Utilized After Technologies 43716.7 Use Case For The Application 43716.7.1 Application Icon 43716.7.2 Enlistment Form 43816.7.3 Login Form 43916.7.4 Misconduct Place Detector 43916.7.5 Help Button 44016.8 Conclusion 443References 44317 Conclusion and Future Direction in Data Mining and Machine Learning 447Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Ramakant Chandrakar17.1 Introduction 44817.2 Machine Learning 45117.2.1 Neural Network 45217.2.2 Deep Learning 45217.2.3 Three Activities for Object Recognition 45317.3 Conclusion 457References 457Index 461

Rohit Raja, PhD is an associate professor in the IT Department, Guru Ghasidas Vishwavidyalaya, Bilaspur (CG), India. He has published more than 80 research papers in peer-reviewed journals as well as 9 patents. Kapil Kumar Nagwanshi, PhD is an associate professor at Mukesh Patel School of Technology Management & Engineering, Shirpur Campus, SVKM’s Narsee Monjee Institute of Management Studies Mumbai, India. Sandeep Kumar, PhD is a professor in the Department of Electronics & Communication Engineering, Sreyas Institute of Engineering & Technology, Hyderabad, India. His area of research includes embedded systems, image processing, and biometrics. He has published more than 60 research papers in peer-reviewed journals as well as 6 patents. K. Ramya Laxmi, PhD is an associate professor in the CSE Department at the Sreyas Institute of Engineering and Technology, Hyderabad. Her research interest covers the fields of data mining and image processing.

• Preface xvii1 Introduction to Data Mining 1Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Sandeep Kumar1.1. Introduction 11.1.1 Data Mining 11.2 Knowledge Discovery in Database (KDD) 21.2.1 Importance of Data Mining 31.2.2 Applications of Data Mining 31.2.3 Databases 41.3 Issues in Data Mining 61.4 Data Mining Algorithms 71.5 Data Warehouse 91.6 Data Mining Techniques 101.7 Data Mining Tools 111.7.1 Python for Data Mining 121.7.2 KNIME 131.7.3 Rapid Miner 17References 182 Classification and Mining Behavior of Data 21Srinivas Konda, Kavitarani Balmuri and Kishore Kumar Mamidala2.1 Introduction 222.2 Main Characteristics of Mining Behavioral Data 232.2.1 Mining Dynamic/Streaming Data 232.2.2 Mining Graph & Network Data 242.2.3 Mining Heterogeneous/Multi-Source Information 252.2.3.1 Multi-Source and Multidimensional Information 262.2.3.2 Multi-Relational Data 262.2.3.3 Background and Connected Data 272.2.3.4 Complex Data, Sequences, and Events 272.2.3.5 Data Protection and Morals 272.2.4 Mining High Dimensional Data 282.2.5 Mining Imbalanced Data 292.2.5.1 The Class Imbalance Issue 292.2.6 Mining Multimedia Data 302.2.6.1 Common Applications Multimedia Data Mining 312.2.6.2 Multimedia Data Mining Utilizations 312.2.6.3 Multimedia Database Management 322.2.7 Mining Scientific Data 342.2.8 Mining Sequential Data 352.2.9 Mining Social Networks 362.2.9.1 Social-Media Data Mining Reasons 392.2.10 Mining Spatial and Temporal Data 402.2.10.1 Utilizations of Spatial and Temporal Data Mining 412.3 Research Method 442.4 Results 482.5 Discussion 492.6 Conclusion 50References 513 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 57Rakhi Seth and Aakanksha Sharaff3.1 Introduction 583.2 Related Work on Different Recommender System 603.2.1 Challenges in RS 653.2.2 Research Questions and Architecture of This Paper 663.2.3 Background 683.2.3.1 The Architecture of Hybrid Approach 693.2.4 Analysis 783.2.4.1 Evaluation Measures 783.2.5 Materials and Methods 813.2.6 Comparative Analysis With Traditional Recommender System 853.2.7 Practical Implications 853.2.8 Conclusion & Future Work 94References 944 Stream Mining: Introduction, Tools & Techniques and Applications 99Naresh Kumar Nagwani4.1 Introduction 1004.2 Data Reduction: Sampling and Sketching 1014.2.1 Sampling 1014.2.2 Sketching 1024.3 Concept Drift 1034.4 Stream Mining Operations 1054.4.1 Clustering 1054.4.2 Classification 1064.4.3 Outlier Detection 1074.4.4 Frequent Itemsets Mining 1084.5 Tools & Techniques 1094.5.1 Implementation in Java 1104.5.2 Implementation in Python 1164.5.3 Implementation in R 1184.6 Applications 1204.6.1 Stock Prediction in Share Market 1204.6.2 Weather Forecasting System 1214.6.3 Finding Trending News and Events 1214.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream) 1214.6.5 Pollution Control Systems 1224.7 Conclusion 122References 1225 Data Mining Tools and Techniques: Clustering Analysis 125Rohit Miri, Amit Kumar Dewangan, S.R. Tandan, Priya Bhatnagar and Hiral Raja5.1 Introduction 1265.2 Data Mining Task 1295.2.1 Data Summarization 1295.2.2 Data Clustering 1295.2.3 Classification of Data 1295.2.4 Data Regression 1305.2.5 Data Association 1305.3 Data Mining Algorithms and Methodologies 1315.3.1 Data Classification Algorithm 1315.3.2 Predication 1325.3.3 Association Rule 1325.3.4 Neural Network 1325.3.4.1 Data Clustering Algorithm 1335.3.5 In-Depth Study of Gathering Techniques 1345.3.6 Data Partitioning Method 1345.3.7 Hierarchical Method 1345.3.8 Framework-Based Method 1365.3.9 Model-Based Method 1365.3.10 Thickness-Based Method 1365.4 Clustering the Nearest Neighbor 1365.4.1 Fuzzy Clustering 1375.4.2 K-Algorithm Means 1375.5 Data Mining Applications 1385.6 Materials and Strategies for Document Clustering 1405.6.1 Features Generation 1425.7 Discussion and Results 1435.7.1 Discussion 1465.7.2 Conclusion 149References 1496 Data Mining Implementation Process 151Kamal K. Mehta, Rajesh Tiwari and Nishant Behar6.1 Introduction 1516.2 Data Mining Historical Trends 1526.3 Processes of Data Analysis 1536.3.1 Data Attack 1536.3.2 Data Mixing 1536.3.3 Data Collection 1536.3.4 Data Conversion 1546.3.4.1 Data Mining 1546.3.4.2 Design Evaluation 1546.3.4.3 Data Illustration 1546.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process 1546.3.5 Business Understanding 1556.3.6 Data Understanding 1566.3.7 Data Preparation 1586.3.8 Modeling 1596.3.9 Evaluation 1606.3.10 Deployment 1616.3.11 Contemporary Developments 1626.3.12 An Assortment of Data Mining 1626.3.12.1 Using Computational & Connectivity Tools 1636.3.12.2 Web Mining 1636.3.12.3 Comparative Statement 1636.3.13 Advantages of Data Mining 1636.3.14 Drawbacks of Data Mining 1656.3.15 Data Mining Applications 1656.3.16 Methodology 1676.3.17 Results 1696.3.18 Conclusion and Future Scope 171References 1727 Predictive Analytics in IT Service Management (ITSM) 175Sharon Christa I.L. and Suma V.7.1 Introduction 1767.2 Analytics: An Overview 1787.2.1 Predictive Analytics 1807.3 Significance of Predictive Analytics in ITSM 1817.4 Ticket Analytics: A Case Study 1867.4.1 Input Parameters 1887.4.2 Predictive Modeling 1887.4.3 Random Forest Model 1897.4.4 Performance of the Predictive Model 1917.5 Conclusion 191References 1928 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques 195K. Ramya Laxmi, Sumit Srivastava, K. Madhuravani, S. Pallavi and Omprakash Dewangan8.1 Introduction 1968.2 Literature Review 1988.3 Methodology and Implementation 2008.3.1 Selection of the Independent Variables 2008.4 Data Partitioning 2038.4.1 Interpreting the Results of Logistic Regression Model 2038.5 Conclusions 204References 2059 Inductive Learning Including Decision Tree and Rule Induction Learning 209Raj Kumar Patra, A. Mahendar and G. Madhukar9.1 Introduction 2109.2 The Inductive Learning Algorithm (ILA) 2129.3 Proposed Algorithms 2139.4 Divide & Conquer Algorithm 2149.4.1 Decision Tree 2149.5 Decision Tree Algorithms 2159.5.1 ID3 Algorithm 2159.5.2 Separate and Conquer Algorithm 2179.5.3 RULE EXTRACTOR-1 2269.5.4 Inductive Learning Applications 2269.5.4.1 Education 2269.5.4.2 Making Credit Decisions 2279.5.5 Multidimensional Databases and OLAP 2289.5.6 Fuzzy Choice Trees 2289.5.7 Fuzzy Choice Tree Development From a Multidimensional Database 2299.5.8 Execution and Results 2309.6 Conclusion and Future Work 231References 23210 Data Mining for Cyber-Physical Systems 235M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi and Shaik Munawar10.1 Introduction 23610.1.1 Models of Cyber-Physical System 23810.1.2 Statistical Model-Based Methodologies 23910.1.3 Spatial-and-Transient Closeness-Based Methodologies 24010.2 Feature Recovering Methodologies 24010.3 CPS vs. IT Systems 24110.4 Collections, Sources, and Generations of Big Data for CPS 24210.4.1 Establishing Conscious Computation and Information Systems 24310.5 Spatial Prediction 24310.5.1 Global Optimization 24410.5.2 Big Data Analysis CPS 24510.5.3 Analysis of Cloud Data 24510.5.4 Analysis of Multi-Cloud Data 24710.6 Clustering of Big Data 24810.7 NoSQL 25110.8 Cyber Security and Privacy Big Data 25110.8.1 Protection of Big Computing and Storage 25210.8.2 Big Data Analytics Protection 25210.8.3 Big Data CPS Applications 25610.9 Smart Grids 25610.10 Military Applications 25810.11 City Management 25910.12 Clinical Applications 26110.13 Calamity Events 26210.14 Data Streams Clustering by Sensors 26310.15 The Flocking Model 26310.16 Calculation Depiction 26410.17 Initialization 26510.18 Representative Maintenance and Clustering 26610.19 Results 26710.20 Conclusion 268References 26911 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining 281Vivek Parganiha, Soorya Prakash Shukla and Lokesh Kumar Sharma11.1 Introduction 28211.2 Background 28311.3 Methodology of CRISP-DM 28411.4 Stage One—Determine Business Objectives 28611.4.1 What Are the Ideal Yields of the Venture? 28711.4.2 Evaluate the Current Circumstance 28811.4.3 Realizes Data Mining Goals 28911.5 Stage Two—Data Sympathetic 29011.5.1 Portray Data 29111.5.2 Investigate Facts 29111.5.3 Confirm Data Quality 29211.5.4 Data Excellence Description 29211.6 Stage Three—Data Preparation 29211.6.1 Select Your Data 29411.6.2 The Data Is Processed 29411.6.3 Data Needed to Build 29411.6.4 Combine Information 29511.7 Stage Four—Modeling 29511.7.1 Select Displaying Strategy 29611.7.2 Produce an Investigation Plan 29711.7.3 Fabricate Ideal 29711.7.4 Evaluation Model 29711.8 Stage Five—Evaluation 29811.8.1 Assess Your Outcomes 29911.8.2 Survey Measure 29911.8.3 Decide on the Subsequent Stages 30011.9 Stage Six—Deployment 30011.9.1 Plan Arrangement 30111.9.2 Plan Observing and Support 30111.9.3 Produce the Last Report 30211.9.4 Audit Venture 30211.10 Data on ERP Systems 30211.11 Usage of CRISP-DM Methodology 30411.12 Modeling 30611.12.1 Association Rule Mining (ARM) or Association Analysis 30711.12.2 Classification Algorithms 30711.12.3 Regression Algorithms 30811.12.4 Clustering Algorithms 30811.13 Assessment 31011.14 Distribution 31011.15 Results and Discussion 31011.16 Conclusion 311References 31412 Human–Machine Interaction and Visual Data Mining 317Upasana Sinha, Akanksha Gupta, Samera Khan, Shilpa Rani and Swati Jain12.1 Introduction 31812.2 Related Researches 32012.2.1 Data Mining 32312.2.2 Data Visualization 32312.2.3 Visual Learning 32412.3 Visual Genes 32512.4 Visual Hypotheses 32612.5 Visual Strength and Conditioning 32612.6 Visual Optimization 32712.7 The Vis 09 Model 32712.8 Graphic Monitoring and Contact With Human–Computer 32812.9 Mining HCI Information Using Inductive Deduction Viewpoint 33212.10 Visual Data Mining Methodology 33412.11 Machine Learning Algorithms for Hand Gesture Recognition 33812.12 Learning 33812.13 Detection 33912.14 Recognition 34012.15 Proposed Methodology for Hand Gesture Recognition 34012.16 Result 34312.17 Conclusion 343References 34413 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection 349Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu13.1 Introduction 34913.2 Literature Survey 35213.3 Methods and Material 35313.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm 35513.4 Experimental Results 35713.5 Libraries Used 35713.6 Comparing Algorithms Based on Decision Boundaries 35713.7 Evaluating Results 35813.8 Conclusion 361References 36114 New Algorithms and Technologies for Data Mining 365Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla and Maguluri Sudeep Joel14.1 Introduction 36614.2 Machine Learning Algorithms 36814.3 Supervised Learning 36814.4 Unsupervised Learning 36914.5 Semi-Supervised Learning 36914.6 Regression Algorithms 37114.7 Case-Based Algorithms 37114.8 Regularization Algorithms 37214.9 Decision Tree Algorithms 37214.10 Bayesian Algorithms 37314.11 Clustering Algorithms 37414.12 Association Rule Learning Algorithms 37514.13 Artificial Neural Network Algorithms 37514.14 Deep Learning Algorithms 37614.15 Dimensionality Reduction Algorithms 37714.16 Ensemble Algorithms 37714.17 Other Machine Learning Algorithms 37814.18 Data Mining Assignments 37814.19 Data Mining Models 38114.20 Non-Parametric & Parametric Models 38114.21 Flexible vs. Restrictive Methods 38214.22 Unsupervised vs. Supervised Learning 38214.23 Data Mining Methods 38414.24 Proposed Algorithm 38714.24.1 Organization Formation Procedure 38714.25 The Regret of Learning Phase 38814.26 Conclusion 392References 39215 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier 397Sudesh Kumar, Rekh Ram Janghel and Satya Prakash Sahu15.1 Introduction 39815.2 Related Work 40015.3 Material and Methods 40115.3.1 Dataset Description 40115.3.2 Proposed Methodology 40315.3.3 Normalization 40415.3.4 Preprocessing Using PCA 40415.3.5 Restricted Boltzmann Machine (RBM) 40615.3.6 Stochastic Binary Units (Bernoulli Variables) 40715.3.7 Training 40815.3.7.1 Gibbs Sampling 40915.3.7.2 Contrastive Divergence (CD) 40915.4 Experimental Framework 41015.5 Experimental Results and Discussion 41215.5.1 Performance Measurement Criteria 41215.5.2 Experimental Results 41215.6 Discussion 41415.7 Conclusion 418References 41916 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques 423Nanda R. Wagh and Sanjay R. Sutar16.1 Introduction 42416.2 Related Work 42416.2.1 WoSApp 42416.2.2 Abhaya 42516.2.3 Women Empowerment 42516.2.4 Nirbhaya 42516.2.5 Glympse 42616.2.6 Fightback 42616.2.7 Versatile-Based 42616.2.8 RFID 42616.2.9 Self-Preservation Framework for WomenBWith Area Following and SMS Alarming Through GSM Network 42616.2.10 Safe: A Women Security Framework 42716.2.11 Intelligent Safety System For Women Security 42716.2.12 A Mobile-Based Women Safety Application 42716.2.13 Self-Salvation—The Women’s Security Module 42716.3 Issue and Solution 42716.3.1 Inspiration 42716.3.2 Issue Statement and Choice of Solution 42816.4 Selection of Data 42816.5 Pre-Preparation Data 43016.5.1 Simulation 43116.5.2 Assessment 43116.5.3 Forecast 43416.6 Application Development 43616.6.1 Methodology 43616.6.2 AI Model 43716.6.3 Innovations Used The Proposed Application Has Utilized After Technologies 43716.7 Use Case For The Application 43716.7.1 Application Icon 43716.7.2 Enlistment Form 43816.7.3 Login Form 43916.7.4 Misconduct Place Detector 43916.7.5 Help Button 44016.8 Conclusion 443References 44317 Conclusion and Future Direction in Data Mining and Machine Learning 447Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Ramakant Chandrakar17.1 Introduction 44817.2 Machine Learning 45117.2.1 Neural Network 45217.2.2 Deep Learning 45217.2.3 Three Activities for Object Recognition 45317.3 Conclusion 457References 457Index 461