Vor Ort
Merkzettel Warenkorb
  • Produktbild: Discovering Knowledge in Data
  • Produktbild: Discovering Knowledge in Data

Discovering Knowledge in Data An Introduction to Data Mining

Aus der Reihe Wiley Series on Methods and Applications in Data Mining

111,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.07.2014

Verlag

John Wiley & Sons

Seitenzahl

336

Maße (L/B/H)

24/16,1/2,3 cm

Gewicht

671 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-0-470-90874-7

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

08.07.2014

Verlag

John Wiley & Sons

Seitenzahl

336

Maße (L/B/H)

24/16,1/2,3 cm

Gewicht

671 g

Auflage

2nd edition

Sprache

Englisch

ISBN

978-0-470-90874-7

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
Die Leseprobe wird geladen.
  • Produktbild: Discovering Knowledge in Data
  • Produktbild: Discovering Knowledge in Data
  • PREFACE xi
     
    CHAPTER 1 AN INTRODUCTION TO DATA MINING 1
     
    1.1 What is Data Mining? 1
     
    1.2 Wanted: Data Miners 2
     
    1.3 The Need for Human Direction of Data Mining 3
     
    1.4 The Cross-Industry Standard Practice for Data Mining 4
     
    1.4.1 Crisp-DM: The Six Phases 5
     
    1.5 Fallacies of Data Mining 6
     
    1.6 What Tasks Can Data Mining Accomplish? 8
     
    1.6.1 Description 8
     
    1.6.2 Estimation 8
     
    1.6.3 Prediction 10
     
    1.6.4 Classification 10
     
    1.6.5 Clustering 12
     
    1.6.6 Association 14
     
    References 14
     
    Exercises 15
     
    CHAPTER 2 DATA PREPROCESSING 16
     
    2.1 Why do We Need to Preprocess the Data? 17
     
    2.2 Data Cleaning 17
     
    2.3 Handling Missing Data 19
     
    2.4 Identifying Misclassifications 22
     
    2.5 Graphical Methods for Identifying Outliers 22
     
    2.6 Measures of Center and Spread 23
     
    2.7 Data Transformation 26
     
    2.8 Min-Max Normalization 26
     
    2.9 Z-Score Standardization 27
     
    2.10 Decimal Scaling 28
     
    2.11 Transformations to Achieve Normality 28
     
    2.12 Numerical Methods for Identifying Outliers 35
     
    2.13 Flag Variables 36
     
    2.14 Transforming Categorical Variables into Numerical Variables 37
     
    2.15 Binning Numerical Variables 38
     
    2.16 Reclassifying Categorical Variables 39
     
    2.17 Adding an Index Field 39
     
    2.18 Removing Variables that are Not Useful 39
     
    2.19 Variables that Should Probably Not Be Removed 40
     
    2.20 Removal of Duplicate Records 41
     
    2.21 A Word About ID Fields 41
     
    The R Zone 42
     
    References 48
     
    Exercises 48
     
    Hands-On Analysis 50
     
    CHAPTER 3 EXPLORATORY DATA ANALYSIS 51
     
    3.1 Hypothesis Testing Versus Exploratory Data Analysis 51
     
    3.2 Getting to Know the Data Set 52
     
    3.3 Exploring Categorical Variables 55
     
    3.4 Exploring Numeric Variables 62
     
    3.5 Exploring Multivariate Relationships 69
     
    3.6 Selecting Interesting Subsets of the Data for Further Investigation 71
     
    3.7 Using EDA to Uncover Anomalous Fields 71
     
    3.8 Binning Based on Predictive Value 72
     
    3.9 Deriving New Variables: Flag Variables 74
     
    3.10 Deriving New Variables: Numerical Variables 77
     
    3.11 Using EDA to Investigate Correlated Predictor Variables 77
     
    3.12 Summary 80
     
    The R Zone 82
     
    Reference 88
     
    Exercises 88
     
    Hands-On Analysis 89
     
    CHAPTER 4 UNIVARIATE STATISTICAL ANALYSIS 91
     
    4.1 Data Mining Tasks in Discovering Knowledge in Data 91
     
    4.2 Statistical Approaches to Estimation and Prediction 92
     
    4.3 Statistical Inference 93
     
    4.4 How Confident are We in Our Estimates? 94
     
    4.5 Confidence Interval Estimation of the Mean 95
     
    4.6 How to Reduce the Margin of Error 97
     
    4.7 Confidence Interval Estimation of the Proportion 98
     
    4.8 Hypothesis Testing for the Mean 99
     
    4.9 Assessing the Strength of Evidence Against the Null Hypothesis 101
     
    4.10 Using Confidence Intervals to Perform Hypothesis Tests 102
     
    4.11 Hypothesis Testing for the Proportion 104
     
    The R Zone 105
     
    Reference 106
     
    Exercises 106
     
    CHAPTER 5 MULTIVARIATE STATISTICS 109
     
    5.1 Two-Sample t-Test for Difference in Means 110
     
    5.2 Two-Sample Z-Test for Difference in Proportions 111
     
    5.3 Test for Homogeneity of Proportions 112
     
    5.4 Chi-Square Test for Goodness of Fit of Multinomial Data 114
     
    5.5 Analysis of Variance 115