Home Page Icon
Home Page
Table of Contents for
Title page
Close
Title page
by Bala Deshpande, Vijay Kotu
Predictive Analytics and Data Mining
Cover image
Title page
Table of Contents
Copyright
Dedication
Foreword
Preface
Acknowledgments
Chapter 1. Introduction
1.1. What Data Mining Is
1.2. What Data Mining is Not
1.3. The Case for Data Mining
1.4. Types of Data Mining
1.5. Data Mining Algorithms
1.6. Roadmap for Upcoming Chapters
Chapter 2. Data Mining Process
2.1. Prior Knowledge
2.2. Data Preparation
2.3. Modeling
2.4. Application
2.5. Knowledge
What’s Next?
Chapter 3. Data Exploration
3.1. Objectives of Data Exploration
3.2. Data Sets
3.3. Descriptive Statistics
3.4. Data Visualization
3.5. Roadmap for Data Exploration
Chapter 4. Classification
4.1. Decision Trees
4.2. Rule Induction
4.3. k-Nearest Neighbors
4.4. Naïve Bayesian
4.5. Artificial Neural Networks
4.6. Support Vector Machines
4.7. Ensemble Learners
Chapter 5. Regression Methods
5.1. Linear Regression
5.2. Logistic Regression
Conclusion
Chapter 6. Association Analysis
6.1. Concepts of Mining Association Rules
6.2. Apriori Algorithm
6.3. FP-Growth Algorithm
Conclusion
Chapter 7. Clustering
Clustering to Describe the Data
Clustering for Preprocessing
7.1. Types of Clustering Techniques
7.2. k-Means Clustering
7.3. DBSCAN Clustering
Chapter 8. Model Evaluation
8.1. Confusion Matrix (or Truth Table)
8.2. Receiver Operator Characteristic (ROC) Curves and Area under the Curve (AUC)
8.3. Lift Curves
8.4. Evaluating The Predictions: Implementation
Conclusion
Chapter 9. Text Mining
9.1. How Text Mining Works
9.2. Implementing Text Mining with Clustering and Classification
Conclusion
Chapter 10. Time Series Forecasting
10.1. Data-Driven Approaches
10.2. Model-Driven Forecasting Methods
Conclusion
Chapter 11. Anomaly Detection
11.1. Anomaly Detection Concepts
11.2. Distance-Based Outlier Detection
11.3. Density-Based Outlier Detection
11.4. Local Outlier Factor
Conclusion
Chapter 12. Feature Selection
12.1. Classifying Feature Selection Methods
12.2. Principal Component Analysis
12.3. Information Theory–Based Filtering for Numeric Data
12.4. Chi-Square-Based Filtering for Categorical Data
12.5. Wrapper-Type Feature Selection
Conclusion
Chapter 13. Getting Started with RapidMiner
13.1. User Interface and Terminology
13.2. Data Importing and Exporting Tools
13.3. Data Visualization Tools
13.4. Data Transformation Tools
13.5. Sampling and Missing Value Tools
13.6. Optimization Tools
Conclusion
Comparison of Data Mining Algorithms
Index
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Cover image
Next
Next Chapter
Table of Contents
Predictive Analytics and Data Mining
Concepts and Practice with RapidMiner
Vijay Kotu
Bala Deshpande, PhD
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset
