rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Intelligent data analysis
    sub title: from data gathering to data comprehension
  name:
    name part: Gupta, Deepak
    name part: active 2015-2016
    role:
      role term: editor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: nju
    date issued: 2020
    copyright date: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: "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"--
  table of contents: 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
  table of contents: 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
  table of contents: 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
  table of contents: 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
  table of contents: 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
  note: edited by Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, Kalpna Sagar.
  note: Includes bibliographical references and index.
  subject:
    topic: Data mining
  subject:
    topic: Computational intelligence
  classification: QA76.9.D343 I57435 2020
  classification: 006.3/12
  related item:
    title info:
      title: The Wiley series in intelligent signal and data processing
  related item:
    title info:
      title: Intelligent data analysis
    origin info:
      publisher: Hoboken, NJ, USA : Wiley, 2020
    identifier: (DLC) 2019056735
  identifier: 9781119544487
  identifier: 1119544483
  identifier: 9781119544463
  identifier: 1119544467
  identifier: 1119544440
  identifier: 9781119544449
  identifier: 9781119544456
  identifier: 2019056736
  identifier: 9820815 IEEE
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 200210
    record change date: 20260214215747.5
    record identifier: in00004686612
    language of cataloging:
      language term: eng