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

ISBN-10: 0470526823

ISBN-13: 9780470526828

Edition: 2nd 2010

Authors: Galit Shmueli, Nitin R. Patel, Peter C. Bruce

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This Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models. Featuring complimentary access to XLMiner, the Microsoft Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of DM techniques are presented with hands-on, business-oriented applications. Exercises and examples, now doubled in number in the second edition, are provided to motivate learning and understanding.
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Book details

List price: $89.50
Edition: 2nd
Copyright year: 2010
Publisher: John Wiley & Sons, Limited
Publication date: 11/19/2010
Binding: Hardcover
Pages: 428
Size: 7.00" wide x 10.00" long x 1.00" tall
Weight: 2.288
Language: English

What Is Data Mining?
Where Is Data Mining Used?
The Origins of Data Mining
The Rapid Growth of Data Mining
Why Are There So Many Different Methods?
Terminology and Notation
Road Maps to This Book
Overview of the Data Mining Process
Core Ideas in Data Mining
Supervised and Unsupervised Learning
The Steps in Data Mining
Preliminary Steps
Building a Model: Example with Linear Regression
Using Excel for Data Mining
Data Exploration and Dimension Reduction
Data Visualization
Uses of Data Visualization
Data Examples
Boston Housing Data
Ridership on Amtrak Trains
Basic Charts: bar charts, line graphs, and scatterplots
Distribution Plots
Heatmaps: visualizing correlations and missing values
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
Specialized Visualizations
Visualizing networked data
Visualizing hierarchical data: treemaps
Visualizing geographical data: maps
Summary of major visualizations and operations, according to data mining goal
Time series forecasting
Unsupervised learning
Dimension Reduction
Practical Considerations
House Prices in Boston
Data Summaries
Correlation Analysis
Reducing the Number of Categories in Categorical Variables
Converting A Categorical Variable to A Numerical Variable
Principal Components Analysis
Breakfast Cereals
Principal Components
Normalizing the Data
Using Principal Components for Classification and Prediction
Dimension Reduction Using Regression Models
Dimension Reduction Using Classification and Regression Trees
Performance Evaluation
Evaluating Classification and Predictive Performance
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
Evaluating Predictive Performance
Benchmark: The Average
Prediction Accuracy Measures
Prediction and Classification Methods
Multiple Linear Regression
Explanatory vs. Predictive Modeling
Estimating the Regression Equation and Prediction
Example: Predicting the Price of Used Toyota Corolla Automobiles
Variable Selection in Linear Regression
Reducing the Number of Predictors
How to Reduce the Number of Predictors
Neighbors (kNN)
The kNN
Determining Neighbors
Classification Rule
Example: Riding Mowers
Choosing k
Setting the Cutoff Value
With More Than 2 Classes
for a Numerical Response
Advantages and Shortcomings of kNN
Naive Bayes
Predicting Fraudulent Financial Reporting
The Practical Difficulty with the Complete (Exact) Bayes Procedure
The Solution: Na‹ve Bayes
Predicting Fraudulent Financial Reports, 2 Predictors
Predicting Delayed Flights
Advantages and Shortcomings of the naive Bayes Classifier
Classification and Regression Trees
Classification Trees
Recursive Partitioning
Riding Mowers
Measures of Impurity
Evaluating the Performance of a Classification Tree
Acceptance of Personal Loan
Avoiding Overfitting
Stopping Tree Growth: CHAID
Pruning the Tree
Classification Rules from Trees
Classification Trees for More Than 2 Classes
Regression Trees
Measuring Impurity
Evaluating Performance
Advantages, Weaknesses, and Extensions
Logistic Regression
The Logistic Regression Model
Example: Acceptance of Personal Loan
Model with a Single Predictor
Estimating the Logistic Model from Data: Computing Parameter
Interpreting Results in Terms of Odds
Evaluating Classification Performance
Variable Selection
Example of Complete Analysis: Predicting Delayed Flights
Data Preprocessing
Model Fitting and Estimation
Model Interpretation
Model Performance
Variable Selection
Appendix: Logistic Regression for Profiling
Appendix: Logistic regression for profiling
Appendix: B: Evaluating Goodness of Fit
Appendix B Evaluating Goodness of Fit
Appendix: C: Logistic Regression for More Than Two Classes
Appendix C Logistic Regression for More Than Two Classes
Neural Nets
Concept and Structure of a Neural Network
Fitting a Network to Data
Tiny Dataset
Computing Output of Nodes
Preprocessing the Data
Training the Model
Classifying Accident Severity
Avoiding overfitting
Using the Output for Prediction and Classification
Required User Input
Exploring the Relationship Between Predictors and Response
Advantages and Weaknesses of Neural Networks
Discriminant Analysis
Riding Mowers
Personal Loan Acceptance
Distance of an Observation from a Class
Fisher's Linear Classification Functions
Classification Performance of Discriminant Analysis
Prior Probabilities
Unequal Misclassification Costs
Classifying More Than Two Classes
Medical Dispatch to Accident Scenes
Advantages and Weaknesses
Mining Relationships Among Records
Association Rules
Discovering Association Rules in Transaction Databases
Synthetic Data on Purchases of Phone Faceplates
Generating Candidate Rules
The Apriori Algorithm
Selecting Strong Rules
Support and Confidence
Lift Ratio
Data Format
The Process of Rule Selection
Interpreting the Results
Statistical Significance of Rules
Rules for Similar Book Purchases
Cluster Analysis
Example: Public Utilities
Measuring Distance Between Two Records
Euclidean Distance
Normalizing Numerical Measurements
Other Distance Measures for Numerical Data
Distance Measures for Categorical Data
Distance Measures for Mixed Data
Measuring Distance Between Two Clusters
Hierarchical (Agglomerative) Clustering
Minimum Distance (Single Linkage)
Maximum Distance (Complete Linkage)
Average Distance (Average Linkage)
Dendrograms: Displaying Clustering Process and Results
Validating Clusters
Limitations of Hierarchical Clustering
Nonhierarchical Clustering: The kMeans Algorithm
Initial Partition into k Clusters
Forecasting Time Series
Handling Time Series
Explanatory vs. Predictive Modeling
Popular Forecasting Methods in Business
Combining Methods
Time Series Components
Example: Ridership on Amtrak Trains
Data Partitioning
Regression Based Forecasting
A Model with Trend
Linear Trend
Exponential Trend
Polynomial Trend
A Model with Seasonality
A model with trend and seasonality
Autocorrelation and ARIMA Models
Computing Autocorrelation
Computing Autocorrelation
Improving Forecasts by Integrating Autocorrelation Information
Improving Forecasts by Integrating Autocorrelation Information
Evaluating Predictability
Evaluating Predictability
Smoothing Methods
Moving Average
Centered Moving Average for Visualization
Trailing Moving Average for Forecasting
Choosing Window Width
Simple Exponential Smoothing
Choosing Smoothing Parameter
Relation Between Moving Average and Simple Exponential
Advanced Exponential Smoothing
Series with a trend
Series with a trend and seasonality
Series with seasonality
Charles Book Club
German Credit
Tayko Software Cataloger
Segmenting Consumers of Bath Soap
DirectMail Fundraising
Catalog CrossSelling
Predicting Bankruptcy
Time Series Case: Forecasting Public Transportation Demand
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