Intelligent data analysis : from data gathering to data comprehension / edited by Deepak Gupta

ABOUT THE AUTHORS
Deepak Gupta completed his PhD (CSE) at Dr. APJ Abdul Kalam Technical University, Lucknow, India; his M.E. (CTA) at Delhi College of Engineering, New Delhi, India; and his B.Tech at Guru Gobind Singh Indraprastha University, Delhi, India. He completed his postdoctoral research on the Internet of Things (IoT) at the National Institute of Telecommunications, Ghaziabad, India. He is a guest editor for SCI and SCOPUS and has co-authored 38 books and published 95 research papers.

Siddhartha Bhattacharyya, PhD, is a Professor of Computer Science at CHRIST (Deemed to be University), Bengaluru, India.

Ashish Khanna received his PhD from the National Institute of Technology, Kurukshetra, India. He completed his M.Tech and B.Tech at Guru Gobind Singh Indraprastha University, Delhi, India in 2004. He has published 100 research papers and co-authored 22 textbooks on engineering. His research includes distributed computing, distributed systems, cloud computing, and opportunistic networks. He completed his postdoctoral research on the Internet of Things (IoT) at the National Institute of Telecommunications, Ghaziabad, India.

Kalpna Sagar received her B.Tech from Indira Gandhi Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India and her M.Tech from University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Delhi, India. She is currently pursuing her PhD. Her research includes software engineering, human-computer interaction, and data-mining. She has published numerous research papers and is currently an Assistant Professor and Assistant Dean of Academics at KIET Group of Institutions, Dr. APJ Abdul Kalam Technical University, Lucknow, India.

Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Series Preface -- Preface -- Chapter 1 Intelligent Data Analysis: Black Box Versus White Box Modeling -- 1.1 Introduction -- 1.1.1 Intelligent Data Analysis -- 1.1.2 Applications of IDA and Machine Learning -- 1.1.3 White Box Models Versus Black Box Models -- 1.1.4 Model Interpretability -- 1.2 Interpretation of White Box Models -- 1.2.1 Linear Regression -- 1.2.2 Decision Tree -- 1.3 Interpretation of Black Box Models -- 1.3.1 Partial Dependence Plot -- 1.3.2 Individual Conditional Expectation

1.3.3 Accumulated Local Effects -- 1.3.4 Global Surrogate Models -- 1.3.5 Local Interpretable Model-Agnostic Explanations -- 1.3.6 Feature Importance -- 1.4 Issues and Further Challenges -- 1.5 Summary -- References -- Chapter 2 Data: Its Nature and Modern Data Analytical Tools -- 2.1 Introduction -- 2.2 Data Types and Various File Formats -- 2.2.1 Structured Data -- 2.2.2 Semi-Structured Data -- 2.2.3 Unstructured Data -- 2.2.4 Need for File Formats -- 2.2.5 Various Types of File Formats -- 2.2.5.1 Comma Separated Values (CSV) -- 2.2.5.2 ZIP -- 2.2.5.3 Plain Text (txt) -- 2.2.5.4 JSON

2.2.5.5 XML -- 2.2.5.6 Image Files -- 2.2.5.7 HTML -- 2.3 Overview of Big Data -- 2.3.1 Sources of Big Data -- 2.3.1.1 Media -- 2.3.1.2 The Web -- 2.3.1.3 Cloud -- 2.3.1.4 Internet of Things -- 2.3.1.5 Databases -- 2.3.1.6 Archives -- 2.3.2 Big Data Analytics -- 2.3.2.1 Descriptive Analytics -- 2.3.2.2 Predictive Analytics -- 2.3.2.3 Prescriptive Analytics -- 2.4 Data Analytics Phases -- 2.5 Data Analytical Tools -- 2.5.1 Microsoft Excel -- 2.5.2 Apache Spark -- 2.5.3 Open Refine -- 2.5.4 R Programming -- 2.5.4.1 Advantages of R -- 2.5.4.2 Disadvantages of R -- 2.5.5 Tableau

2.5.5.1 How TableauWorks -- 2.5.5.2 Tableau Feature -- 2.5.5.3 Advantages -- 2.5.5.4 Disadvantages -- 2.5.6 Hadoop -- 2.5.6.1 Basic Components of Hadoop -- 2.5.6.2 Benefits -- 2.6 Database Management System for Big Data Analytics -- 2.6.1 Hadoop Distributed File System -- 2.6.2 NoSql -- 2.6.2.1 Categories of NoSql -- 2.7 Challenges in Big Data Analytics -- 2.7.1 Storage of Data -- 2.7.2 Synchronization of Data -- 2.7.3 Security of Data -- 2.7.4 Fewer Professionals -- 2.8 Conclusion -- References -- Chapter 3 Statistical Methods for Intelligent Data Analysis: Introduction and Various Concepts

3.1 Introduction -- 3.2 Probability -- 3.2.1 Definitions -- 3.2.1.1 Random Experiments -- 3.2.1.2 Probability -- 3.2.1.3 Probability Axioms -- 3.2.1.4 Conditional Probability -- 3.2.1.5 Independence -- 3.2.1.6 Random Variable -- 3.2.1.7 Probability Distribution -- 3.2.1.8 Expectation -- 3.2.1.9 Variance and Standard Deviation -- 3.2.2 Bayes' Rule -- 3.3 Descriptive Statistics -- 3.3.1 Picture Representation -- 3.3.1.1 Frequency Distribution -- 3.3.1.2 Simple Frequency Distribution -- 3.3.1.3 Grouped Frequency Distribution -- 3.3.1.4 Stem and Leaf Display -- 3.3.1.5 Histogram and Bar Chart

"The new tool for analyses is?Intelligent Data Analysis (IDA)?. IDA can be defined as the use of specialized statistical, pattern recognition, machine learning, data abstraction, and visualization tools for analysis of data and discovery of mechanisms that created the data. Such data are typically complex, meaning that they are characterized by many records, many variables, subtle interactions between variables, or a combination of all three. Engineering, computing sciences, database science, machine learning, and even artificial intelligence are bringing their powers to this newly born data analysis discipline. The main idea underlying the concept of Intelligent Data Analysis is extracting knowledge from a very large amount of data, with a very large amount of variables; data that represents very complex, non-linear, real-life problems. Moreover, IDA can help when starting from the raw data, coping with prediction tasks without knowing the theoretical description of the underlying process, classification tasks of new events based on past ones, or modeling the aforementioned unknown process. Classification, prediction, and modeling are the cornerstones that Intelligent Data Analysis can bring to us"-- Provided by publisher

Online resource; title from digital title page (viewed on October 24, 2023).