(Ebook) Data Mining for Business Intelligence Concepts Techniques and Applications in Microsoft Office Excel r with XLMiner r 2nd Edition by Patel, Nitin,Bruce, Peter,Shmueli, Galit ISBN 978-0470526828 0470526823

(Ebook) Data Mining for Business Intelligence Concepts Techniques and Applications in Microsoft Office Excel r with XLMiner r 2nd Edition by Patel, Nitin,Bruce, Peter,Shmueli, Galit ISBN 978-0470526828 0470526823

(Ebook) Data Mining for Business Intelligence Concepts Techniques and Applications in Microsoft Office Excel r with XLMiner r 2nd Edition by Patel, Nitin R.,Bruce, Peter C.,Shmueli, Galit  - Ebook PDF Instant Download/Delivery:  978-0470526828, 0470526823
Full download (Ebook) Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel (r) with XLMiner (r) 2nd Edition after payment

Product details: 

ISBN 10:   0470526823

ISBN 13: 978-0470526828

Author:  Patel, Nitin R.;Bruce, Peter C.;Shmueli, Galit

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 techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data.
From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization.
The Second Edition now features:
Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles
A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice
Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods
Summaries at the start of each chapter that supply an outline of key topics
The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions.
Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

Table of contents: 

PART I 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 Regression

2.7 Using Excel for Data Mining

PROBLEMS

PART II 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 Variables

4.6 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 III PERFORMANCE EVALUATION

Chapter 5 Evaluating Classification and Predictive Performance

5.1 Introduction

5.2 Judging Classification Performance

5.3 Evaluating Predictive Performance

PROBLEMS

PART IV PREDICTION AND CLASSIFICATION METHODS

Chapter 6 Multiple Linear Regression

6.1 Introduction

6.2 Explanatory versus Predictive modeling

6.3 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

9.3 Measures of Impurity

9.4 Evaluating the Performance of a Classification Tree

9.5 Avoiding Overfitting

9.6 Classification Rules from Trees

9.7 Classification Trees for More Than two Classes

9.8 Regression Trees

9.9 Advantages, weaknesses, and Extensions

PROBLEMS

Chapter 10 Logistic Regression

10.1 Introduction

10.2 Logistic Regression Model

10.3 Evaluating Classification performance

10.4 Example of Complete Analysis: Predicting Delayed Flights

10.5 Appendix: logistic Regression for Profiling

PROBLEMS

Chapter 11 Neural Nets

11.1 Introduction

11.2 Concept And Structure Of A Neural Network

11.3 Fitting A Network To Data

11.4 Required User Input

11.5 Exploring The Relationship Between Predictors And Response

11.6 Advantages And Weaknesses Of Neural Networks

PROBLEMS

Chapter 12 Discriminant Analysis

12.1 Introduction

12.2 Distance of an Observation from a Class

12.3 Fisher's Linear Classification Functions

12.4 Classification performance of Discriminant Analysis

12.5 Prior Probabilities

12.6 Unequal Misclassification Costs

12.7 Classifying more Than Two Classes

12.8 Advantages and Weaknesses

PROBLEMS

PART V MINING RELATIONSHIPS AMONG RECORDS

Chapter 13 Association Rules

13.1 Introduction

13.2 Discovering Association Rules in Transaction Databases

13.3 Generating Candidate Rules

13.4 Selecting Strong Rules

13.5 Summary

PROBLEMS

Chapter 14 Cluster Analysis

14.1 Introduction

14.2 Measuring Distance Between Two Records

14.3 Measuring Distance Between Two Clusters

14.4 Hierarchical (Agglomerative) Clustering

14.5 Nonhierarchical Clustering: The k-Means Algorithm

PROBLEMS

PART VI FORECASTING TIME SERIES

Chapter 15 Handling Time Series

15.1 Introduction

15.2 Explanatory versus Predictive Modeling

15.3 Popular Forecasting Methods in Business

15.4 Time Series Components

15.5 Data Partitioning

PROBLEMS

Chapter 16 Regression-Based Forecasting

16.1 Model With Trend

16.2 Model With Seasonality

16.3 Model With Trend And Seasonality

16.4 Autocorrelation And ARIMA Models

PROBLEMS

Chapter 17 Smoothing Methods

17.1 Introduction

17.2 Moving Average

17.3 Simple Exponential Smoothing

17.4 Advanced Exponential Smoothing

PROBLEMS

PART VII CASES

Chapter 18 Cases

18.1 Charles book Club

18.2 German Credit

18.3 Tayko Software Cataloger

18.4 Segmenting Consumers of Bath Soap

18.5 Direct-Mail Fundraising

18.6 Catalog Cross Selling

18.7 Predicting Bankruptcy

18.8 Time Series Case: Forecasting Public Transportation Demand

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Tags: Patel, Nitin, Bruce, Peter, Shmueli, Galit, Data Mining, Business Intelligence