Learning Data Mining with R -- Table of Contents -- Learning Data Mining with R -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers, and more -- Why subscribe? -- Free access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Errata -- Piracy -- Questions -- 1. Warming Up -- Big data -- Scalability and efficiency -- Data source -- Data mining -- Feature extraction -- Summarization -- The data mining process -- CRISP-DM -- SEMMA -- Social network mining -- Social network -- Text mining -- Information retrieval and text mining -- Mining text for prediction -- Web data mining -- Why R? -- What are the disadvantages of R? -- Statistics -- Statistics and data mining -- Statistics and machine learning -- Statistics and R -- The limitations of statistics on data mining -- Machine learning -- Approaches to machine learning -- Machine learning architecture -- Data attributes and description -- Numeric attributes -- Categorical attributes -- Data description -- Data measuring -- Data cleaning -- Missing values -- Junk, noisy data, or outlier -- Data integration -- Data dimension reduction -- Eigenvalues and Eigenvectors -- Principal-Component Analysis -- Singular-value decomposition -- CUR decomposition -- Data transformation and discretization -- Data transformation -- Normalization data transformation methods -- Data discretization -- Visualization of results -- Visualization with R -- Time for action -- Summary -- 2. Mining Frequent Patterns, Associations, and Correlations -- An overview of associations and patterns -- Patterns and pattern discovery -- The frequent itemset -- The frequent subsequence.

The frequent substructures -- Relationship or rules discovery -- Association rules -- Correlation rules -- Market basket analysis -- The market basket model -- A-Priori algorithms -- Input data characteristics and data structure -- The A-Priori algorithm -- The R implementation -- A-Priori algorithm variants -- The Eclat algorithm -- The R implementation -- The FP-growth algorithm -- Input data characteristics and data structure -- The FP-growth algorithm -- The R implementation -- The GenMax algorithm with maximal frequent itemsets -- The R implementation -- The Charm algorithm with closed frequent itemsets -- The R implementation -- The algorithm to generate association rules -- The R implementation -- Hybrid association rules mining -- Mining multilevel and multidimensional association rules -- Constraint-based frequent pattern mining -- Mining sequence dataset -- Sequence dataset -- The GSP algorithm -- The R implementation -- The SPADE algorithm -- The R implementation -- Rule generation from sequential patterns -- High-performance algorithms -- Time for action -- Summary -- 3. Classification -- Classification -- Generic decision tree induction -- Attribute selection measures -- Tree pruning -- General algorithm for the decision tree generation -- The R implementation -- High-value credit card customers classification using ID3 -- The ID3 algorithm -- The R implementation -- Web attack detection -- High-value credit card customers classification -- Web spam detection using C4.5 -- The C4.5 algorithm -- The R implementation -- A parallel version with MapReduce -- Web spam detection -- Web key resource page judgment using CART -- The CART algorithm -- The R implementation -- Web key resource page judgment -- Trojan traffic identification method and Bayes classification -- Estimating -- Prior probability estimation -- Likelihood estimation.

The Bayes classification -- The R implementation -- Trojan traffic identification method -- Identify spam e-mail and Naïve Bayes classification -- The Naïve Bayes classification -- The R implementation -- Identify spam e-mail -- Rule-based classification of player types in computer games and rule-based classification -- Transformation from decision tree to decision rules -- Rule-based classification -- Sequential covering algorithm -- The RIPPER algorithm -- The R implementation -- Rule-based classification of player types in computer games -- Time for action -- Summary -- 4. Advanced Classification -- Ensemble (EM) methods -- The bagging algorithm -- The boosting and AdaBoost algorithms -- The Random forests algorithm -- The R implementation -- Parallel version with MapReduce -- Biological traits and the Bayesian belief network -- The Bayesian belief network (BBN) algorithm -- The R implementation -- Biological traits -- Protein classification and the k-Nearest Neighbors algorithm -- The kNN algorithm -- The R implementation -- Document retrieval and Support Vector Machine -- The SVM algorithm -- The R implementation -- Parallel version with MapReduce -- Document retrieval -- Classification using frequent patterns -- The associative classification -- CBA -- Discriminative frequent pattern-based classification -- The R implementation -- Text classification using sentential frequent itemsets -- Classification using the backpropagation algorithm -- The BP algorithm -- The R implementation -- Parallel version with MapReduce -- Time for action -- Summary -- 5. Cluster Analysis -- Search engines and the k-means algorithm -- The k-means clustering algorithm -- The kernel k-means algorithm -- The k-modes algorithm -- The R implementation -- Parallel version with MapReduce -- Search engine and web page clustering.

Automatic abstraction of document texts and the k-medoids algorithm -- The PAM algorithm -- The R implementation -- Automatic abstraction and summarization of document text -- The CLARA algorithm -- The CLARA algorithm -- The R implementation -- CLARANS -- The CLARANS algorithm -- The R implementation -- Unsupervised image categorization and affinity propagation clustering -- Affinity propagation clustering -- The R implementation -- Unsupervised image categorization -- The spectral clustering algorithm -- The R implementation -- News categorization and hierarchical clustering -- Agglomerative hierarchical clustering -- The BIRCH algorithm -- The chameleon algorithm -- The Bayesian hierarchical clustering algorithm -- The probabilistic hierarchical clustering algorithm -- The R implementation -- News categorization -- Time for action -- Summary -- 6. Advanced Cluster Analysis -- Customer categorization analysis of e-commerce and DBSCAN -- The DBSCAN algorithm -- Customer categorization analysis of e-commerce -- Clustering web pages and OPTICS -- The OPTICS algorithm -- The R implementation -- Clustering web pages -- Visitor analysis in the browser cache and DENCLUE -- The DENCLUE algorithm -- The R implementation -- Visitor analysis in the browser cache -- Recommendation system and STING -- The STING algorithm -- The R implementation -- Recommendation systems -- Web sentiment analysis and CLIQUE -- The CLIQUE algorithm -- The R implementation -- Web sentiment analysis -- Opinion mining and WAVE clustering -- The WAVE cluster algorithm -- The R implementation -- Opinion mining -- User search intent and the EM algorithm -- The EM algorithm -- The R implementation -- The user search intent -- Customer purchase data analysis and clustering high-dimensional data -- The MAFIA algorithm -- The SURFING algorithm -- The R implementation.

Customer purchase data analysis -- SNS and clustering graph and network data -- The SCAN algorithm -- The R implementation -- Social networking service (SNS) -- Time for action -- Summary -- 7. Outlier Detection -- Credit card fraud detection and statistical methods -- The likelihood-based outlier detection algorithm -- The R implementation -- Credit card fraud detection -- Activity monitoring - the detection of fraud involving mobile phones and proximity-based methods -- The NL algorithm -- The FindAllOutsM algorithm -- The FindAllOutsD algorithm -- The distance-based algorithm -- The Dolphin algorithm -- The R implementation -- Activity monitoring and the detection of mobile fraud -- Intrusion detection and density-based methods -- The OPTICS-OF algorithm -- The High Contrast Subspace algorithm -- The R implementation -- Intrusion detection -- Intrusion detection and clustering-based methods -- Hierarchical clustering to detect outliers -- The k-means-based algorithm -- The ODIN algorithm -- The R implementation -- Monitoring the performance of the web server and classification-based methods -- The OCSVM algorithm -- The one-class nearest neighbor algorithm -- The R implementation -- Monitoring the performance of the web server -- Detecting novelty in text, topic detection, and mining contextual outliers -- The conditional anomaly detection (CAD) algorithm -- The R implementation -- Detecting novelty in text and topic detection -- Collective outliers on spatial data -- The route outlier detection (ROD) algorithm -- The R implementation -- Characteristics of collective outliers -- Outlier detection in high-dimensional data -- The brute-force algorithm -- The HilOut algorithm -- The R implementation -- Time for action -- Summary -- 8. Mining Stream, Time-series, and Sequence Data -- The credit card transaction flow and STREAM algorithm.

The STREAM algorithm.