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Produktbild: Data Mining for Business Intelligence

Data Mining for Business Intelligence Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

Fr. 138.00

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

27.11.2010

Verlag

John Wiley & Sons Inc

Seitenzahl

392

Maße (L/B/H)

26.1/18.8/2.6 cm

Gewicht

924 g

Auflage

2. Auflage

Sprache

Englisch

ISBN

978-0-470-52682-8

Beschreibung

Rezension

"The book would be useful for a one- or two-semester data mining course or a business intelligence course." (The American Statistician, 1 November 2011)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

27.11.2010

Verlag

John Wiley & Sons Inc

Seitenzahl

392

Maße (L/B/H)

26.1/18.8/2.6 cm

Gewicht

924 g

Auflage

2. Auflage

Sprache

Englisch

ISBN

978-0-470-52682-8

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  • Produktbild: Data Mining for Business Intelligence
  • Foreword by Daryl Pregibon (Google, Inc.).

    Preface.

    Acknowledgments.

    PART I PRELIMINARIES.

    1 Introduction.

    1.1 What Is Data Mining?

    1.2 Where Is Data Mining Used?

    1.3 The Origins of Data Mining.

    1.4 The Rapid Growth of Data Mining.

    1.5 Why Are There So Many Different Methods?

    1.6 Terminology and Notation.

    1.7 Road Maps to This Book.

    2 Overview of the Data Mining Process.

    2.1 Introduction.

    2.2 Core Ideas in Data Mining.

    2.3 Supervised and Unsupervised Learning.

    2.4 The Steps in Data Mining.

    2.5 Preliminary Steps.

    2.6 Building a Model: Example with Linear Regression.

    2.7 Using Excel for Data Mining.

    Problems.

    PART II DATA EXPLORATION AND DIMENSION REDUCTION.

    3 Data Visualization.

    3.1 Uses of Data Visualization.

    3.2 Data Examples.

    Example 1: Boston Housing Data.

    Example 2: Ridership on Amtrak Trains.

    3.3 Basic Charts: bar charts, line graphs, and scatterplots.

    Distribution Plots.

    Heatmaps: visualizing correlations and missing values.

    3.4 MultiDimensional Visualization.

    Adding Variables: color, hue, size, shape, multiple panels, animation.

    Manipulations: rescaling,aggregation and hierarchies, zooming and panning, filtering.

    Reference: trend line and labels.

    Scaling up: large datasets.

    Multivariate plot: parallel coordinates plot.

    Interactive visualization.

    3.5 Specialized Visualizations.

    Visualizing networked data.

    Visualizing hierarchical data: treemaps.

    Visualizing geographical data: maps.

    3.6 Summary of major visualizations and operations, according to data mining goal.

    Prediction.

    Classification.

    Time series forecasting.

    Unsupervised learning.

    Problems.

    4 Dimension Reduction.

    4.1 Introduction.

    4.2 Practical Considerations.

    Example 1: House Prices in Boston.
    4.3 Data Summaries.

    4.4 Correlation Analysis.

    4.5 Reducing the Number of Categories in Categorical Variables.

    4.6 Converting A Categorical Variable to A Numerical Variable.

    4.7 Principal Components Analysis.

    Example 2: Breakfast Cereals.

    Principal Components.

    Normalizing the Data.

    Using Principal Components for Classification and Prediction.

    4.8 Dimension Reduction Using Regression Models.

    4.9 Dimension Reduction Using Classification and Regression Trees.

    Problems.

    PART III PERFORMANCE EVALUATION.

    5 Evaluating Classification and Predictive Performance.

    5.1 Introduction.

    5.2 Judging Classification Performance.

    Benchmark: The Naive Rule.

    Class Separation.

    The Classification Matrix.

    Using the Validation Data.

    Accuracy Measures.

    Cutoff for Classification.

    Performance in Unequal Importance of Classes.

    Asymmetric Misclassification Costs.

    Oversampling and Asymmetric Costs.

    Classification Using a Triage Strategy.

    5.3 Evaluating Predictive Performance.

    Benchmark: The Average.

    Prediction Accuracy Measures.

    Problems.

    PART IV PREDICTION AND CLASSIFICATION METHODS.

    6 Multiple Linear Regression.

    6.1 Introduction.

    6.2 Explanatory vs. Predictive Modeling.

    6.3 Estimating the Regression Equation and Prediction.

    Example: Predicting the Price of Used Toyota Corolla Automobiles.

    6.4 Variable Selection in Linear Regression.

    Reducing the Number of Predictors.

    How to Reduce the Number of Predictors.

    Problems.

    7 kNearest.

    Neighbors (kNN).

    7.1 The kNN.

    Classifier (categorical outcome).

    Determining Neighbors.

    Classification Rule.

    Example: Riding Mowers.

    Choosing k.

    Setting the Cutoff Value.

    kNN.

    With More Than 2 Classes.

    7.2 kNN.

    for a Numerical Response.

    7.3 Advantages and Shortcomings of kNN.

    Algorithms.

    Problems.

    8 Naive Bayes.

    8.1 Introduction.

    Example 1: Predicting Fraudulent Financial Reporting.

    The Practical Difficulty with the Complete (Exact) Bayes Procedure.

    The Solution: Na've Bayes.

    Example 2: Predicting Fraudulent Financial Reports, 2 Predictors.
    Example 3: P