Intelligent data analysis : (Record no. 69260)

000 -LEADER
fixed length control field 06401cam a22006138i 4500
001 - CONTROL NUMBER
control field on1149370344
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220711203600.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200210s2020 nju ob 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119544487
-- (electronic bk. : oBook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119544483
-- (electronic bk. : oBook)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119544463
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119544467
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119544449
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119544440
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (hardback)
029 1# - (OCLC)
OCLC library identifier AU@
System control number 000067015568
082 00 - CLASSIFICATION NUMBER
Call Number 006.3/12
245 00 - TITLE STATEMENT
Title Intelligent data analysis :
Sub Title from data gathering to data comprehension /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource.
490 0# - SERIES STATEMENT
Series statement The Wiley series in intelligent signal and data processing
520 ## - SUMMARY, ETC.
Summary, etc "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"--
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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
505 8# - FORMATTED CONTENTS NOTE
Remark 2 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
505 8# - FORMATTED CONTENTS NOTE
Remark 2 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
505 8# - FORMATTED CONTENTS NOTE
Remark 2 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
505 8# - FORMATTED CONTENTS NOTE
Remark 2 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
590 ## - LOCAL NOTE (RLIN)
Local note John Wiley and Sons
700 1# - AUTHOR 2
Author 2 Gupta, Deepak,
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1002/9781119544487
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Hoboken, NJ, USA :
-- Wiley,
-- 2020.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- n
-- rdamedia
338 ## -
-- online resource
-- nc
-- rdacarrier
520 ## - SUMMARY, ETC.
-- Provided by publisher.
588 ## -
-- Description based on print version record and CIP data provided by publisher; resource not viewed.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
-- (OCoLC)fst00871995
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
-- (OCoLC)fst00887946
994 ## -
-- 92
-- DG1

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