Table of Contents
1.6. Roadmap for Upcoming Chapters
Chapter 2. Data Mining Process
3.1. Objectives of Data Exploration
3.5. Roadmap for Data Exploration
4.5. Artificial Neural Networks
Chapter 6. Association Analysis
6.1. Concepts of Mining Association Rules
Clustering to Describe the Data
7.1. Types of Clustering Techniques
8.1. Confusion Matrix (or Truth Table)
8.2. Receiver Operator Characteristic (ROC) Curves and Area under the Curve (AUC)
8.4. Evaluating The Predictions: Implementation
9.2. Implementing Text Mining with Clustering and Classification
Chapter 10. Time Series Forecasting
10.2. Model-Driven Forecasting Methods
11.1. Anomaly Detection Concepts
11.2. Distance-Based Outlier Detection
11.3. Density-Based Outlier Detection
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
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