Data Mining and Machine Learning Applications

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.

Table of Contents:
Preface xvii 1 Introduction to Data Mining 1 Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Sandeep Kumar 1.1. Introduction 1 1.1.1 Data Mining 1 1.2 Knowledge Discovery in Database (KDD) 2 1.2.1 Importance of Data Mining 3 1.2.2 Applications of Data Mining 3 1.2.3 Databases 4 1.3 Issues in Data Mining 6 1.4 Data Mining Algorithms 7 1.5 Data Warehouse 9 1.6 Data Mining Techniques 10 1.7 Data Mining Tools 11 1.7.1 Python for Data Mining 12 1.7.2 KNIME 13 1.7.3 Rapid Miner 17 References 18 2 Classification and Mining Behavior of Data 21 Srinivas Konda, Kavitarani Balmuri and Kishore Kumar Mamidala 2.1 Introduction 22 2.2 Main Characteristics of Mining Behavioral Data 23 2.2.1 Mining Dynamic/Streaming Data 23 2.2.2 Mining Graph & Network Data 24 2.2.3 Mining Heterogeneous/Multi-Source Information 25 2.2.3.1 Multi-Source and Multidimensional Information 26 2.2.3.2 Multi-Relational Data 26 2.2.3.3 Background and Connected Data 27 2.2.3.4 Complex Data, Sequences, and Events 27 2.2.3.5 Data Protection and Morals 27 2.2.4 Mining High Dimensional Data 28 2.2.5 Mining Imbalanced Data 29 2.2.5.1 The Class Imbalance Issue 29 2.2.6 Mining Multimedia Data 30 2.2.6.1 Common Applications Multimedia Data Mining 31 2.2.6.2 Multimedia Data Mining Utilizations 31 2.2.6.3 Multimedia Database Management 32 2.2.7 Mining Scientific Data 34 2.2.8 Mining Sequential Data 35 2.2.9 Mining Social Networks 36 2.2.9.1 Social-Media Data Mining Reasons 39 2.2.10 Mining Spatial and Temporal Data 40 2.2.10.1 Utilizations of Spatial and Temporal Data Mining 41 2.3 Research Method 44 2.4 Results 48 2.5 Discussion 49 2.6 Conclusion 50 References 51 3 A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects 57 Rakhi Seth and Aakanksha Sharaff 3.1 Introduction 58 3.2 Related Work on Different Recommender System 60 3.2.1 Challenges in RS 65 3.2.2 Research Questions and Architecture of This Paper 66 3.2.3 Background 68 3.2.3.1 The Architecture of Hybrid Approach 69 3.2.4 Analysis 78 3.2.4.1 Evaluation Measures 78 3.2.5 Materials and Methods 81 3.2.6 Comparative Analysis With Traditional Recommender System 85 3.2.7 Practical Implications 85 3.2.8 Conclusion & Future Work 94 References 94 4 Stream Mining: Introduction, Tools & Techniques and Applications 99 Naresh Kumar Nagwani 4.1 Introduction 100 4.2 Data Reduction: Sampling and Sketching 101 4.2.1 Sampling 101 4.2.2 Sketching 102 4.3 Concept Drift 103 4.4 Stream Mining Operations 105 4.4.1 Clustering 105 4.4.2 Classification 106 4.4.3 Outlier Detection 107 4.4.4 Frequent Itemsets Mining 108 4.5 Tools & Techniques 109 4.5.1 Implementation in Java 110 4.5.2 Implementation in Python 116 4.5.3 Implementation in R 118 4.6 Applications 120 4.6.1 Stock Prediction in Share Market 120 4.6.2 Weather Forecasting System 121 4.6.3 Finding Trending News and Events 121 4.6.4 Analyzing User Behavior in Electronic Commerce Site (Click Stream) 121 4.6.5 Pollution Control Systems 122 4.7 Conclusion 122 References 122 5 Data Mining Tools and Techniques: Clustering Analysis 125 Rohit Miri, Amit Kumar Dewangan, S.R. Tandan, Priya Bhatnagar and Hiral Raja 5.1 Introduction 126 5.2 Data Mining Task 129 5.2.1 Data Summarization 129 5.2.2 Data Clustering 129 5.2.3 Classification of Data 129 5.2.4 Data Regression 130 5.2.5 Data Association 130 5.3 Data Mining Algorithms and Methodologies 131 5.3.1 Data Classification Algorithm 131 5.3.2 Predication 132 5.3.3 Association Rule 132 5.3.4 Neural Network 132 5.3.4.1 Data Clustering Algorithm 133 5.3.5 In-Depth Study of Gathering Techniques 134 5.3.6 Data Partitioning Method 134 5.3.7 Hierarchical Method 134 5.3.8 Framework-Based Method 136 5.3.9 Model-Based Method 136 5.3.10 Thickness-Based Method 136 5.4 Clustering the Nearest Neighbor 136 5.4.1 Fuzzy Clustering 137 5.4.2 K-Algorithm Means 137 5.5 Data Mining Applications 138 5.6 Materials and Strategies for Document Clustering 140 5.6.1 Features Generation 142 5.7 Discussion and Results 143 5.7.1 Discussion 146 5.7.2 Conclusion 149 References 149 6 Data Mining Implementation Process 151 Kamal K. Mehta, Rajesh Tiwari and Nishant Behar 6.1 Introduction 151 6.2 Data Mining Historical Trends 152 6.3 Processes of Data Analysis 153 6.3.1 Data Attack 153 6.3.2 Data Mixing 153 6.3.3 Data Collection 153 6.3.4 Data Conversion 154 6.3.4.1 Data Mining 154 6.3.4.2 Design Evaluation 154 6.3.4.3 Data Illustration 154 6.3.4.4 Implementation of Data Mining in the Cross-Industry Standard Process 154 6.3.5 Business Understanding 155 6.3.6 Data Understanding 156 6.3.7 Data Preparation 158 6.3.8 Modeling 159 6.3.9 Evaluation 160 6.3.10 Deployment 161 6.3.11 Contemporary Developments 162 6.3.12 An Assortment of Data Mining 162 6.3.12.1 Using Computational & Connectivity Tools 163 6.3.12.2 Web Mining 163 6.3.12.3 Comparative Statement 163 6.3.13 Advantages of Data Mining 163 6.3.14 Drawbacks of Data Mining 165 6.3.15 Data Mining Applications 165 6.3.16 Methodology 167 6.3.17 Results 169 6.3.18 Conclusion and Future Scope 171 References 172 7 Predictive Analytics in IT Service Management (ITSM) 175 Sharon Christa I.L. and Suma V. 7.1 Introduction 176 7.2 Analytics: An Overview 178 7.2.1 Predictive Analytics 180 7.3 Significance of Predictive Analytics in ITSM 181 7.4 Ticket Analytics: A Case Study 186 7.4.1 Input Parameters 188 7.4.2 Predictive Modeling 188 7.4.3 Random Forest Model 189 7.4.4 Performance of the Predictive Model 191 7.5 Conclusion 191 References 192 8 Modified Cross-Sell Model for Telecom Service Providers Using Data Mining Techniques 195 K. Ramya Laxmi, Sumit Srivastava, K. Madhuravani, S. Pallavi and Omprakash Dewangan 8.1 Introduction 196 8.2 Literature Review 198 8.3 Methodology and Implementation 200 8.3.1 Selection of the Independent Variables 200 8.4 Data Partitioning 203 8.4.1 Interpreting the Results of Logistic Regression Model 203 8.5 Conclusions 204 References 205 9 Inductive Learning Including Decision Tree and Rule Induction Learning 209 Raj Kumar Patra, A. Mahendar and G. Madhukar 9.1 Introduction 210 9.2 The Inductive Learning Algorithm (ILA) 212 9.3 Proposed Algorithms 213 9.4 Divide & Conquer Algorithm 214 9.4.1 Decision Tree 214 9.5 Decision Tree Algorithms 215 9.5.1 ID3 Algorithm 215 9.5.2 Separate and Conquer Algorithm 217 9.5.3 RULE EXTRACTOR-1 226 9.5.4 Inductive Learning Applications 226 9.5.4.1 Education 226 9.5.4.2 Making Credit Decisions 227 9.5.5 Multidimensional Databases and OLAP 228 9.5.6 Fuzzy Choice Trees 228 9.5.7 Fuzzy Choice Tree Development From a Multidimensional Database 229 9.5.8 Execution and Results 230 9.6 Conclusion and Future Work 231 References 232 10 Data Mining for Cyber-Physical Systems 235 M. Varaprasad Rao, D. Anji Reddy, Anusha Ampavathi and Shaik Munawar 10.1 Introduction 236 10.1.1 Models of Cyber-Physical System 238 10.1.2 Statistical Model-Based Methodologies 239 10.1.3 Spatial-and-Transient Closeness-Based Methodologies 240 10.2 Feature Recovering Methodologies 240 10.3 CPS vs. IT Systems 241 10.4 Collections, Sources, and Generations of Big Data for CPS 242 10.4.1 Establishing Conscious Computation and Information Systems 243 10.5 Spatial Prediction 243 10.5.1 Global Optimization 244 10.5.2 Big Data Analysis CPS 245 10.5.3 Analysis of Cloud Data 245 10.5.4 Analysis of Multi-Cloud Data 247 10.6 Clustering of Big Data 248 10.7 NoSQL 251 10.8 Cyber Security and Privacy Big Data 251 10.8.1 Protection of Big Computing and Storage 252 10.8.2 Big Data Analytics Protection 252 10.8.3 Big Data CPS Applications 256 10.9 Smart Grids 256 10.10 Military Applications 258 10.11 City Management 259 10.12 Clinical Applications 261 10.13 Calamity Events 262 10.14 Data Streams Clustering by Sensors 263 10.15 The Flocking Model 263 10.16 Calculation Depiction 264 10.17 Initialization 265 10.18 Representative Maintenance and Clustering 266 10.19 Results 267 10.20 Conclusion 268 References 269 11 Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining 281 Vivek Parganiha, Soorya Prakash Shukla and Lokesh Kumar Sharma 11.1 Introduction 282 11.2 Background 283 11.3 Methodology of CRISP-DM 284 11.4 Stage One—Determine Business Objectives 286 11.4.1 What Are the Ideal Yields of the Venture? 287 11.4.2 Evaluate the Current Circumstance 288 11.4.3 Realizes Data Mining Goals 289 11.5 Stage Two—Data Sympathetic 290 11.5.1 Portray Data 291 11.5.2 Investigate Facts 291 11.5.3 Confirm Data Quality 292 11.5.4 Data Excellence Description 292 11.6 Stage Three—Data Preparation 292 11.6.1 Select Your Data 294 11.6.2 The Data Is Processed 294 11.6.3 Data Needed to Build 294 11.6.4 Combine Information 295 11.7 Stage Four—Modeling 295 11.7.1 Select Displaying Strategy 296 11.7.2 Produce an Investigation Plan 297 11.7.3 Fabricate Ideal 297 11.7.4 Evaluation Model 297 11.8 Stage Five—Evaluation 298 11.8.1 Assess Your Outcomes 299 11.8.2 Survey Measure 299 11.8.3 Decide on the Subsequent Stages 300 11.9 Stage Six—Deployment 300 11.9.1 Plan Arrangement 301 11.9.2 Plan Observing and Support 301 11.9.3 Produce the Last Report 302 11.9.4 Audit Venture 302 11.10 Data on ERP Systems 302 11.11 Usage of CRISP-DM Methodology 304 11.12 Modeling 306 11.12.1 Association Rule Mining (ARM) or Association Analysis 307 11.12.2 Classification Algorithms 307 11.12.3 Regression Algorithms 308 11.12.4 Clustering Algorithms 308 11.13 Assessment 310 11.14 Distribution 310 11.15 Results and Discussion 310 11.16 Conclusion 311 References 314 12 Human–Machine Interaction and Visual Data Mining 317 Upasana Sinha, Akanksha Gupta, Samera Khan, Shilpa Rani and Swati Jain 12.1 Introduction 318 12.2 Related Researches 320 12.2.1 Data Mining 323 12.2.2 Data Visualization 323 12.2.3 Visual Learning 324 12.3 Visual Genes 325 12.4 Visual Hypotheses 326 12.5 Visual Strength and Conditioning 326 12.6 Visual Optimization 327 12.7 The Vis 09 Model 327 12.8 Graphic Monitoring and Contact With Human–Computer 328 12.9 Mining HCI Information Using Inductive Deduction Viewpoint 332 12.10 Visual Data Mining Methodology 334 12.11 Machine Learning Algorithms for Hand Gesture Recognition 338 12.12 Learning 338 12.13 Detection 339 12.14 Recognition 340 12.15 Proposed Methodology for Hand Gesture Recognition 340 12.16 Result 343 12.17 Conclusion 343 References 344 13 MSDTrA: A Boosting Based-Transfer Learning Approach for Class Imbalanced Skin Lesion Dataset for Melanoma Detection 349 Lokesh Singh, Rekh Ram Janghel and Satya Prakash Sahu 13.1 Introduction 349 13.2 Literature Survey 352 13.3 Methods and Material 353 13.3.1 Proposed Methodology: Multi Source Dynamic TrAdaBoost Algorithm 355 13.4 Experimental Results 357 13.5 Libraries Used 357 13.6 Comparing Algorithms Based on Decision Boundaries 357 13.7 Evaluating Results 358 13.8 Conclusion 361 References 361 14 New Algorithms and Technologies for Data Mining 365 Padma Bonde, Latika Pinjarkar, Korhan Cengiz, Aditi Shukla and Maguluri Sudeep Joel 14.1 Introduction 366 14.2 Machine Learning Algorithms 368 14.3 Supervised Learning 368 14.4 Unsupervised Learning 369 14.5 Semi-Supervised Learning 369 14.6 Regression Algorithms 371 14.7 Case-Based Algorithms 371 14.8 Regularization Algorithms 372 14.9 Decision Tree Algorithms 372 14.10 Bayesian Algorithms 373 14.11 Clustering Algorithms 374 14.12 Association Rule Learning Algorithms 375 14.13 Artificial Neural Network Algorithms 375 14.14 Deep Learning Algorithms 376 14.15 Dimensionality Reduction Algorithms 377 14.16 Ensemble Algorithms 377 14.17 Other Machine Learning Algorithms 378 14.18 Data Mining Assignments 378 14.19 Data Mining Models 381 14.20 Non-Parametric & Parametric Models 381 14.21 Flexible vs. Restrictive Methods 382 14.22 Unsupervised vs. Supervised Learning 382 14.23 Data Mining Methods 384 14.24 Proposed Algorithm 387 14.24.1 Organization Formation Procedure 387 14.25 The Regret of Learning Phase 388 14.26 Conclusion 392 References 392 15 Classification of EEG Signals for Detection of Epileptic Seizure Using Restricted Boltzmann Machine Classifier 397 Sudesh Kumar, Rekh Ram Janghel and Satya Prakash Sahu 15.1 Introduction 398 15.2 Related Work 400 15.3 Material and Methods 401 15.3.1 Dataset Description 401 15.3.2 Proposed Methodology 403 15.3.3 Normalization 404 15.3.4 Preprocessing Using PCA 404 15.3.5 Restricted Boltzmann Machine (RBM) 406 15.3.6 Stochastic Binary Units (Bernoulli Variables) 407 15.3.7 Training 408 15.3.7.1 Gibbs Sampling 409 15.3.7.2 Contrastive Divergence (CD) 409 15.4 Experimental Framework 410 15.5 Experimental Results and Discussion 412 15.5.1 Performance Measurement Criteria 412 15.5.2 Experimental Results 412 15.6 Discussion 414 15.7 Conclusion 418 References 419 16 An Enhanced Security of Women and Children Using Machine Learning and Data Mining Techniques 423 Nanda R. Wagh and Sanjay R. Sutar 16.1 Introduction 424 16.2 Related Work 424 16.2.1 WoSApp 424 16.2.2 Abhaya 425 16.2.3 Women Empowerment 425 16.2.4 Nirbhaya 425 16.2.5 Glympse 426 16.2.6 Fightback 426 16.2.7 Versatile-Based 426 16.2.8 RFID 426 16.2.9 Self-Preservation Framework for WomenBWith Area Following and SMS Alarming Through GSM Network 426 16.2.10 Safe: A Women Security Framework 427 16.2.11 Intelligent Safety System For Women Security 427 16.2.12 A Mobile-Based Women Safety Application 427 16.2.13 Self-Salvation—The Women’s Security Module 427 16.3 Issue and Solution 427 16.3.1 Inspiration 427 16.3.2 Issue Statement and Choice of Solution 428 16.4 Selection of Data 428 16.5 Pre-Preparation Data 430 16.5.1 Simulation 431 16.5.2 Assessment 431 16.5.3 Forecast 434 16.6 Application Development 436 16.6.1 Methodology 436 16.6.2 AI Model 437 16.6.3 Innovations Used The Proposed Application Has Utilized After Technologies 437 16.7 Use Case For The Application 437 16.7.1 Application Icon 437 16.7.2 Enlistment Form 438 16.7.3 Login Form 439 16.7.4 Misconduct Place Detector 439 16.7.5 Help Button 440 16.8 Conclusion 443 References 443 17 Conclusion and Future Direction in Data Mining and Machine Learning 447 Santosh R. Durugkar, Rohit Raja, Kapil Kumar Nagwanshi and Ramakant Chandrakar 17.1 Introduction 448 17.2 Machine Learning 451 17.2.1 Neural Network 452 17.2.2 Deep Learning 452 17.2.3 Three Activities for Object Recognition 453 17.3 Conclusion 457 References 457 Index 461