Intelligent Data Analysis : From Data Gathering to Data Comprehension /: From Data Gathering to Data Comprehension. (2020)

• Record Type: Book
• Further Information: Note: Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, Kalpna Sagar.
• Editors: Gupta, Deepak, active 2015-2016; Bhattacharyya, Siddhartha; Khanna, Ashish; Sagar, Kalpna
• Contents: List of Contributors xix Series Preface xxiii Preface xxv 1 Intelligent Data Analysis: Black Box Versus White Box Modeling 1; Sarthak Gupta, Siddhant Bagga, and Deepak Kumar Sharma 1.1 Introduction 1 1.1.1 Intelligent Data Analysis 1 1.1.2 Applications of IDA and Machine Learning 2 1.1.3 White Box Models Versus Black Box Models 2 1.1.4 Model Interpretability 3 1.2 Interpretation of White Box Models 3 1.2.1 Linear Regression 3 1.2.2 Decision Tree 5 1.3 Interpretation of Black Box Models 7 1.3.1 Partial Dependence Plot 7 1.3.2 Individual Conditional Expectation 9 1.3.3 Accumulated Local Effects 9 1.3.4 Global Surrogate Models 12 1.3.5 Local Interpretable Model-Agnostic Explanations 12 1.3.6 Feature Importance 12 1.4 Issues and Further Challenges 13 1.5 Summary 13 References 14 2 Data: Its Nature and Modern Data Analytical Tools 17; Ravinder Ahuja, Shikhar Asthana, Ayush Ahuja, and Manu Agarwal 2.1 Introduction 17 2.2 Data Types and Various File Formats 18 2.2.1 Structured Data 18 2.2.2 Semi-Structured Data 20 2.2.3 Unstructured Data 20 2.2.4 Need for File Formats 21 2.2.5 Various Types of File Formats 22 2.2.5.1 Comma Separated Values (CSV) 22 2.2.5.2 ZIP 22 2.2.5.3 Plain Text (txt) 23 2.2.5.4 JSON 23 2.2.5.5 XML 23 2.2.5.6 Image Files 24 2.2.5.7 HTML 24 2.3 Overview of Big Data 25 2.3.1 Sources of Big Data 27 2.3.1.1 Media 27 2.3.1.2 The Web 27 2.3.1.3 Cloud 27 2.3.1.4 Internet of Things 27 2.3.1.5 Databases 27 2.3.1.6 Archives 28 2.3.2 Big Data Analytics 28 2.3.2.1 Descriptive Analytics 28 2.3.2.2 Predictive Analytics 28 2.3.2.3 Prescriptive Analytics 29 2.4 Data Analytics Phases 29 2.5 Data Analytical Tools 30 2.5.1 Microsoft Excel 30 2.5.2 Apache Spark 33 2.5.3 Open Refine 34 2.5.4 R Programming 35 2.5.4.1 Advantages of R 36 2.5.4.2 Disadvantages of R 36 2.5.5 Tableau 36 2.5.5.1 How TableauWorks 36 2.5.5.2 Tableau Feature 37 2.5.5.3 Advantages 37 2.5.5.4 Disadvantages 37 2.5.6 Hadoop 37 2.5.6.1 Basic Components of Hadoop 38 2.5.6.2 Benefits 38 2.6 Database Management System for Big Data Analytics 38 2.6.1 Hadoop Distributed File System 38 2.6.2 NoSql 38 2.6.2.1 Categories of NoSql 39 2.7 Challenges in Big Data Analytics 39 2.7.1 Storage of Data 40 2.7.2 Synchronization of Data 40 2.7.3 Security of Data 40 2.7.4 Fewer Professionals 40 2.8 Conclusion 40 References 41 3 Statistical Methods for Intelligent Data Analysis: Introduction and Various Concepts 43; Shubham Kumaram, Samarth Chugh, and Deepak Kumar Sharma 3.1 Introduction 43 3.2 Probability 43 3.2.1 Definitions 43 3.2.1.1 Random Experiments 43 3.2.1.2 Probability 44 3.2.1.3 Probability Axioms 44 3.2.1.4 Conditional Probability 44 3.2.1.5 Independence 44 3.2.1.6 Random Variable 44 3.2.1.7 Probability Distribution 45 3.2.1.8 Expectation 45 3.2.1.9 Variance and Standard Deviation 45 3.2.2 Bayes’ Rule 45 3.3 Descriptive Statistics 46 3.3.1 Picture Representation 46 3.3.1.1 Frequency Distribution 46 3.3.1.2 Simple Frequency Distribution 46 3.3.1.3 Grouped Frequency Distribution 46 3.3.1.4 Stem and Leaf Display 46 3.3.1.5 Histogram and Bar Chart 47 3.3.2 Measures of Central Tendency 47 3.3.2.1 Mean 47 3.3.2.2 Median 47 3.3.2.3 Mode 47 3.3.3 Measures of Variability 48 3.3.3.1 Range 48 3.3.3.2 Box Plot 48 3.3.3.3 Variance and Standard Deviation 48 3.3.4 Skewness and Kurtosis 48 3.4 Inferential Statistics 49 3.4.1 Frequentist Inference 49 3.4.1.1 Point Estimation 50 3.4.1.2 Interval Estimation 50 3.4.2 Hypothesis Testing 51 3.4.3 Statistical Significance 51 3.5 Statistical Methods 52 3.5.1 Regression 52 3.5.1.1 Linear Model 52 3.5.1.2 Nonlinear Models 52 3.5.1.3 Generalized Linear Models 53 3.5.1.4 Analysis of Variance 53 3.5.1.5 Multivariate Analysis of Variance 55 3.5.1.6 Log-Linear Models 55 3.5.1.7 Logistic Regression 56 3.5.1.8 Random Effects Model 56 3.5.1.9 Overdispersion 57 3.5.1.10 Hierarchical Models 57 3.5.2 Analysis of Survival Data 57 3.5.3 Principal Component Analysis 58 3.6 Errors 59 3.6.1 Error in Regression 60 3.6.2 Error in Classification 61 3.7 Conclusion 61 References 61 4 Intelligent Data Analysis with Data Mining: Theory and Applications 63; Shivam Bachhety, Ramneek Singhal, and Rachna Jain Objective 63 4.1 Introduction to Data Mining 63 4.1.1 Importance of Intelligent Data Analytics in Business 64 4.1.2 Importance of Intelligent Data Analytics in Health Care 65 4.2 Data and Knowledge 65 4.3 Discovering Knowledge in Data Mining 66 4.3.1 Process Mining 67 4.3.2 Process of Knowledge Discovery 67 4.4 Data Analysis and Data Mining 69 4.5 Data Mining: Issues 69 4.6 Data Mining: Systems and Query Language 71 4.6.1 Data Mining Systems 71 4.6.2 Data Mining Query Language 72 4.7 Data Mining Methods 73 4.7.1 Classification 74 4.7.2 Cluster Analysis 75 4.7.3 Association 75 4.7.4 Decision Tree Induction 76 4.8 Data Exploration 77 4.9 Data Visualization 80 4.10 Probability Concepts for Intelligent Data Analysis (IDA) 83 Reference 83 5 Intelligent Data Analysis: Deep Learning and Visualization 85<br /> Than D. Le and Huy V. Pham 5.1 Introduction 85 5.2 Deep Learning and Visualization 86 5.2.1 Linear and Logistic Regression and Visualization 86 5.2.2 CNN Architecture 89 5.2.2.1 Vanishing Gradient Problem 90 5.2.2.2 Convolutional Neural Networks (CNNs) 91 5.2.3 Reinforcement Learning 91 5.2.4 Inception and ResNet Networks 93 5.2.5 Softmax 94 5.3 Data Processing and Visualization 97 5.3.1 Regularization for Deep Learning and Visualization 98 5.3.1.1 Regularization for Linear Regression 98 5.4 Experiments and Results 102 5.4.1 Mask RCNN Based on Object Detection and Segmentation 102 5.4.2 Deep Matrix Factorization 108 5.4.2.1 Network Visualization 108 5.4.3 Deep Learning and Reinforcement Learning 111 5.5 Conclusion 112 References 113 6 A Systematic Review on the Evolution of Dental Caries Detection Methods and Its Significance in Data Analysis Perspective 115; Soma Datta, Nabendu Chaki, and Biswajit Modak 6.1 Introduction 115 6.1.1 Analysis of Dental Caries 115 6.2 Different Caries Lesion Detection Methods and Data Characterization 119 6.2.1 Point Detection Method 120 6.2.2 Visible Light Property Method 121 6.2.3 Radiographs 121 6.2.4 Light-Emitting Devices 123 6.2.5 Optical Coherent T
• Edition: 1st
• Publisher Details: Wiley
• Publication Date: 2020
• Extent: 1 online resource (432 pages)
• Languages: English
• ISBNs: 9781119544463
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.