- Learning Data Mining with R
- Table of Contents
- Learning Data Mining with R
- Credits
- About the Author
- Acknowledgments
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Warming Up
- 2. Mining Frequent Patterns, Associations, and Correlations
- An overview of associations and patterns
- Market basket analysis
- Hybrid association rules mining
- Mining sequence dataset
- The R implementation
- High-performance algorithms
- Time for action
- Summary
- 3. Classification
- Classification
- Generic decision tree induction
- High-value credit card customers classification using ID3
- Web spam detection using C4.5
- Web key resource page judgment using CART
- Trojan traffic identification method and Bayes classification
- Identify spam e-mail and Naïve Bayes classification
- Rule-based classification of player types in computer games and rule-based classification
- Time for action
- Summary
- 4. Advanced Classification
- 5. Cluster Analysis
- 6. Advanced Cluster Analysis
- Customer categorization analysis of e-commerce and DBSCAN
- Clustering web pages and OPTICS
- Visitor analysis in the browser cache and DENCLUE
- Recommendation system and STING
- Web sentiment analysis and CLIQUE
- Opinion mining and WAVE clustering
- User search intent and the EM algorithm
- Customer purchase data analysis and clustering high-dimensional data
- SNS and clustering graph and network data
- Time for action
- Summary
- 7. Outlier Detection
- Credit card fraud detection and statistical methods
- Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
- Intrusion detection and density-based methods
- Intrusion detection and clustering-based methods
- Monitoring the performance of the web server and classification-based methods
- Detecting novelty in text, topic detection, and mining contextual outliers
- Collective outliers on spatial data
- Outlier detection in high-dimensional data
- Time for action
- Summary
- 8. Mining Stream, Time-series, and Sequence Data
- 9. Graph Mining and Network Analysis
- 10. Mining Text and Web Data
- A. Algorithms and Data Structures
- Index
Here are some practices for you to check what you've learned so far:
- Running the R code of the ID3 algorithm step by step upon a minor dataset to trace the values of the important factors at each step
- Preparing the dataset related to web logs and creating an application that detects web attacks using ID3
- Implementing an R code to generate decision rules from a decision tree
- What is Gain Ratio?
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