Bater Makhabel - Learning Data Mining with R [2015, PDF, ENG] + Code

Preface
Chapter 1: Warming Up
Big data
Data source
Data mining
Social network mining
Text mining
Web data mining
Why R?
Statistics
Machine learning
Data attributes and description
Data cleaning
Data integration
Data dimension reduction
Data transformation and discretization
Visualization of results
Time for action
Chapter 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
Chapter 3: Classifiation
Classifiation
Generic decision tree induction
High-value credit card customers classifiation using ID3
Web spam detection using C4.5
Web key resource page judgment using CART
Trojan traffi identifiation method and Bayes classifiation
Identify spam e-mail and Naïve Bayes classifiation
Rule-based classifiation of player types in computer games and rule-based classifiation
Time for action
Chapter 4: Advanced Classifiation
Ensemble (EM) methods
Biological traits and the Bayesian belief network
Protein classifiation and the k-Nearest Neighbors algorithm
Document retrieval and Support Vector Machine
Classifiation using frequent patterns
Classifiation using the backpropagation algorithm
Time for action
Chapter 5: Cluster Analysis
Search engines and the k-means algorithm
Automatic abstraction of document texts and the k-medoids algorithm
The CLARA algorithm
CLARANS
Unsupervised image categorization and affiity propagation clustering
News categorization and hierarchical clustering
Time for action
Chapter 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
Chapter 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 classifiation-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
Chapter 8: Mining Stream, Time-series, and Sequence Data
The credit card transaction flw and STREAM algorithm
Predicting future prices and time-series analysis
Stock market data and time-series clustering and classifiation
Web click streams and mining symbolic sequences
Mining sequence patterns in transactional databases
Time for action
Chapter 9: Graph Mining and Network Analysis
Graph mining
Mining frequent subgraph patterns
Social network mining
Time for action
Chapter 10: Mining Text and Web Data
Text mining and TM packages
Text summarization
The question answering system
Genre categorization of web pages
Categorizing newspaper articles and newswires into topics
Web usage mining with web logs
Time for action
Appendix: Algorithms and Data Structures
Index