Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner / Galit Shmueli, Nitin R. Patel, Peter C. Bruce.

Format:

Book

Author/Creator:

Shmueli, Galit, author.

Patel, Nitin R., author.

Bruce, Peter C., author.

Language:

English

Subjects (All):

Microsoft Excel (Computer file).

Data mining.

Business--Data processing.

Business.

Physical Description:

1 online resource (726 p.)

Edition:

2nd ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2010.

Language Note:

English

System Details:

text file

Summary:

Praise for the First Edition "" full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing.""-Research magazine ""Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature.""-computingreviews.com Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techn

Contents:

Cover; Title Page; Dedication; Copyright Page; Foreword; Preface to the second edition; Preface to the first edition; Acknowledgments; Part One: Preliminaries; Chapter 1: Introduction; 1.1 What Is Data Mining?; 1.2 Where Is Data Mining Used?; 1.3 Origins of Data Mining; 1.4 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; Chapter 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 Steps in Data Mining; 2.5 Preliminary Steps

2.6 Building a Model: Example with Linear Regression2.7 Using Excel for Data Mining; PROBLEMS; Part Two: Data Exploration and Dimension Reduction; Chapter 3: Data Visualization; 3.1 Uses of Data Visualization; 3.2 Data Examples; 3.3 Basic Charts: bar charts, line graphs, and scatterplots; 3.4 Multidimensional Visualization; 3.5 Specialized Visualizations; 3.6 Summary of major visualizations and operations, according to data mining goal; PROBLEMS; Chapter 4: Dimension Reduction; 4.1 Introduction; 4.2 Practical Considerations; 4.3 Data Summaries; 4.4 Correlation Analysis

4.5 Reducing the Number of Categories in Categorical Variables4.6 Converting A Categorical Variable to A Numerical Variable; 4.7 Principal Components Analysis; 4.8 Dimension Reduction Using Regression Models; 4.9 Dimension Reduction Using Classification and Regression Trees; PROBLEMS; Part Three: Performance Evaluation; Chapter 5: Evaluating Classification and Predictive Performance; 5.1 Introduction; 5.2 Judging Classification Performance; 5.3 Evaluating Predictive Performance; PROBLEMS; Part Four: Prediction and Classification Methods; Chapter 6: Multiple Linear Regression; 6.1 Introduction

6.2 Explanatory versus Predictive modeling6.3 Estimating the Regression Equation and Prediction; 6.4 Variable Selection in Linear Regression; PROBLEMS; Chapter 7: k-Nearest Neighbors (k-NN); 7.1 k-NN Classifier (categorical outcome); 7.2 k-NN for a Numerical Response; 7.3 Advantages and Shortcomings of k-NN Algorithms; PROBLEMS; Chapter 8: Naive Bayes; 8.1 Introduction; 8.2 Applying the Full (Exact) Bayesian Classifier; 8.3 Advantages and Shortcomings of the Naive Bayes Classifier; PROBLEMS; Chapter 9: Classification and Regression Trees; 9.1 Introduction; 9.2 Classification Trees

11.2 Concept And Structure Of A Neural Network

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed July 27, 2016).

ISBN:

9781118211397

1118211391

OCLC:

811498511