Index

A

• A-Priori algorithm
  • about / A-Priori algorithms
  • input data characteristics / Input data characteristics and data structure
  • data structure / Input data characteristics and data structure
  • join action / The A-Priori algorithm
  • prune action / The A-Priori algorithm
  • R implementation / The R implementation
  • variants / A-Priori algorithm variants
• AdaBoost algorithm
  • about / The boosting and AdaBoost algorithms
• affinity propagation (AP) clustering
  • about / Unsupervised image categorization and affinity propagation clustering
  • R implementation / The R implementation
  • used, for unsupervised image categorization / Unsupervised image categorization
  • spectral clustering algorithm / The spectral clustering algorithm
• agglomerative clustering
  • about / News categorization and hierarchical clustering
  • pseudocode / Agglomerative hierarchical clustering
• algorithm, for association rule generation
  • R implementation / The R implementation
• association rules
  • about / Association rules
  • generating, with algorithm / The algorithm to generate association rules
• associations
  • about / An overview of associations and patterns
• associative classification
  • about / The associative classification
  • Classification Based on Association (CBA) / The associative classification
  • Classification Based on Multiple Association Rules (CMAR) / The associative classification
• attribute
  • about / Data attributes and description
• Auto Regressive Integrated Moving Average (ARIMA) algorithm
  • about / Predicting future prices and time-series analysis, The ARIMA algorithm
  • future prices, predicting / Predicting future prices

B

• bagging algorithm
  • about / The bagging algorithm
  • input parameters / The bagging algorithm
• basket
  • about / The frequent itemset
• Bayes classification
  • about / Trojan traffic identification method and Bayes classification
  • used, for Trojan traffic identification / Trojan traffic identification method and Bayes classification, Trojan traffic identification method
  • prior probability estimation / Prior probability estimation
  • likelihood estimation / Likelihood estimation
  • pseudocode / The Bayes classification
  • R implementation / The R implementation
• Bayesian hierarchical clustering algorithm
  • about / The Bayesian hierarchical clustering algorithm
  • pseudocode / The Bayesian hierarchical clustering algorithm
• BBN algorithm
  • about / Biological traits and the Bayesian belief network, The Bayesian belief network (BBN) algorithm
  • R implementation / The R implementation
  • biological traits / Biological traits
• big data
  • about / Big data
  • data types / Big data
  • scalability / Scalability and efficiency
  • efficiency / Scalability and efficiency
• binning
  • about / Junk, noisy data, or outlier
• BIRCH algorithm
  • about / The BIRCH algorithm
  • CF-Tree rebuilding / The BIRCH algorithm
  • CF-Tree insertion / The BIRCH algorithm
  • pseudocode / The BIRCH algorithm
• Bonferroni's Principle
  • about / The limitations of statistics on data mining
• Bonferroni correction
  • about / The limitations of statistics on data mining
• boosting algorithm
  • about / The boosting and AdaBoost algorithms
• BP algorithm
  • about / Classification using the backpropagation algorithm
  • input parameters / The BP algorithm
  • pseudocode / The BP algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
• brute-force algorithm
  • about / The brute-force algorithm

C

• C4.5 algorithm
  • used, for web spam detection / Web spam detection using C4.5, Web spam detection
  • characteristics / Web spam detection using C4.5
  • pseudocode / The C4.5 algorithm
  • R implementation / The R implementation
  • parallel version, MapReduce / A parallel version with MapReduce
• CART algorithm
  • used, for web key resource page judgment / Web key resource page judgment using CART, Web key resource page judgment
  • about / Web key resource page judgment using CART
  • characteristics / Web key resource page judgment using CART
  • pseudocode / The CART algorithm
  • R implementation / The R implementation
• categorical attributes
  • about / Categorical attributes
  • nominal / Categorical attributes
  • ordinal / Categorical attributes
• CF-Tree
  • about / The BIRCH algorithm
• chameleon algorithm
  • about / The chameleon algorithm
  • sparsification / The chameleon algorithm
  • graph partitioning / The chameleon algorithm
  • agglomerative hierarchical clustering / The chameleon algorithm
• Charm algorithm
  • about / The Charm algorithm with closed frequent itemsets
  • R implementation / The R implementation
• CLARA algorithm
  • about / The CLARA algorithm
  • pseudocode / The CLARA algorithm
  • R implementation / The R implementation
• CLARANS algorithm
  • about / CLARANS
  • input parameters / The CLARANS algorithm
  • pseudocode / The CLARANS algorithm
  • R implementation / The R implementation
• classification
  • about / Classification
  • training (supervised learning) / Classification
  • validation / Classification
• classification, with frequent patterns
  • about / Classification using frequent patterns
  • associative classification / The associative classification
  • discriminative frequent pattern-based classification / Discriminative frequent pattern-based classification
  • R implementation / The R implementation
  • sentential frequent itemsets / Text classification using sentential frequent itemsets
• classification-based methods
  • about / Monitoring the performance of the web server and classification-based methods
  • OCSVM (One Class SVM) algorithm / The OCSVM algorithm
  • one-class nearest neighbor algorithm / The one-class nearest neighbor algorithm
  • R implementation / The R implementation
  • web server performance, monitoring / Monitoring the performance of the web server
• Classification Based on Association (CBA)
  • about / CBA
  • pseudocode / CBA
• Classification Based on Multiple Association Rules (CMAR)
  • about / The associative classification
• CLIQUE algorithm
  • about / Web sentiment analysis and CLIQUE
  • characteristics / Web sentiment analysis and CLIQUE
  • pseudocode / The CLIQUE algorithm
  • R implementation / The R implementation
  • web sentiment analysis / Web sentiment analysis
• closed frequent itemsets
  • mining, with Charm algorithm / The Charm algorithm with closed frequent itemsets
• clustering-based methods
  • about / Intrusion detection and clustering-based methods
  • hierarchical clustering algorithm / Hierarchical clustering to detect outliers
  • k-means algorithm / The k-means-based algorithm
  • ODIN algorithm / The ODIN algorithm
  • R implementation / The R implementation
• collective outliers
  • about / Collective outliers on spatial data
  • route outlier detection (ROD) algorithm / The route outlier detection (ROD) algorithm
  • characteristics / Characteristics of collective outliers
• Comprehensive R Archive Network (CRAN)
  • about / What are the disadvantages of R?
• conditional anomaly detection (CAD) algorithm
  • about / The conditional anomaly detection (CAD) algorithm
  • R implementation / The R implementation
• conditional probability tables (CPT)
  • about / Biological traits and the Bayesian belief network
• constraint-based frequent pattern mining
  • about / Hybrid association rules mining, Constraint-based frequent pattern mining
• contextual outliers
  • mining / Detecting novelty in text, topic detection, and mining contextual outliers
  • conditional anomaly detection (CAD) algorithm / The conditional anomaly detection (CAD) algorithm
• continuous, numeric attributes
  • about / Numeric attributes
• correlation rules
  • about / Correlation rules
• credit card fraud detection
  • about / Credit card fraud detection
• credit card transaction flow
  • mining / The credit card transaction flow
• CRISP-DM
  • about / The data mining process
  • business understanding / CRISP-DM
  • data understanding / CRISP-DM
  • data preparation / CRISP-DM
  • modeling / CRISP-DM
  • evaluation / CRISP-DM
  • deployment / CRISP-DM
• CRM (Customer Relation Management)
  • about / Web click streams
• Cubic Clustering Criterion
  • about / The k-means-based algorithm
• CUR decomposition
  • about / CUR decomposition
• customer purchase data analysis
  • about / Customer purchase data analysis

D

• DASL
  • about / Data source
  • URL / Data source
• data attributes
  • about / Data attributes and description
  • numeric attributes / Numeric attributes
  • categorical attributes / Categorical attributes
• data attributes, views
  • algebraic or geometric view / Data attributes and description
  • probability view / Data attributes and description
• data classification
  • linearly separable / Document retrieval and Support Vector Machine
  • nonlinearly separable / Document retrieval and Support Vector Machine
• data cleaning
  • about / Data cleaning
  • missing values, avoiding / Missing values
  • junk / Junk, noisy data, or outlier
  • noisy data / Junk, noisy data, or outlier
  • outlier / Junk, noisy data, or outlier
• data description
  • about / Data attributes and description, Data description
  • measures of central tendency / Data description
  • measures of data dispersion / Data description
• data dimension reduction
  • about / Data dimension reduction
  • eigenvalues / Eigenvalues and Eigenvectors
  • eigenvectors / Eigenvalues and Eigenvectors
  • PCA / Principal-Component Analysis
  • SVD / Singular-value decomposition
  • CUR decomposition / CUR decomposition
• data discretization
  • about / Data transformation and discretization, Data discretization
  • by binning / Data discretization
  • by histogram analysis / Data discretization
  • by cluster analysis / Data discretization
  • by decision tree analysis / Data discretization
  • by correlation analysis / Data discretization
• data integration
  • about / Data integration
  • issues / Data integration
• data measuring
  • about / Data measuring
• data mining
  • about / Data mining
  • feature extraction / Feature extraction
  • summarization / Summarization
  • process / The data mining process
  • statistics / Statistics and data mining
• Data Quality (DQ)
  • about / Data cleaning
• dataset
  • link-based features / Web spam detection
  • content-based features / Web spam detection
• data smoothing
  • binning / Junk, noisy data, or outlier
  • regression / Junk, noisy data, or outlier
  • classification / Junk, noisy data, or outlier
  • outlier / Junk, noisy data, or outlier
• data source
  • about / Data source
  • online resources / Data source
• data transformation
  • about / Data transformation and discretization, Data transformation
  • smoothing / Data transformation
  • attribute construction / Data transformation
  • aggregation / Data transformation
  • normalization / Data transformation
  • discretization / Data transformation
  • concept hierarchy generation, for nominal data / Data transformation
  • normalization methods / Normalization data transformation methods
• DBSCAN algorithm
  • about / Customer categorization analysis of e-commerce and DBSCAN
  • characteristics / Customer categorization analysis of e-commerce and DBSCAN
  • pseudocode / The DBSCAN algorithm
  • customer categorization analysis, of e-commerce / Customer categorization analysis of e-commerce
• decision tree
  • about / Generic decision tree induction
• decision tree induction
  • about / Generic decision tree induction
  • characteristics / Generic decision tree induction
  • attribute selection measures / Attribute selection measures
  • tree pruning / Tree pruning
  • algorithm, pseudocode / General algorithm for the decision tree generation
  • R implementation / The R implementation
• decision tree induction, attribute selection measures
  • Entropy / Attribute selection measures
  • Gain / Attribute selection measures
  • Gain Ratio / Attribute selection measures
  • Information Gain / Attribute selection measures
  • Gini Index / Attribute selection measures
  • Split Info / Attribute selection measures
• DENCLUE algorithm
  • about / Visitor analysis in the browser cache and DENCLUE
  • density attractor / Visitor analysis in the browser cache and DENCLUE
  • influence function / Visitor analysis in the browser cache and DENCLUE
  • density function / Visitor analysis in the browser cache and DENCLUE
  • gradient / Visitor analysis in the browser cache and DENCLUE
  • pseudocode / The DENCLUE algorithm
  • R implementation / The R implementation
  • visitor analysis, in browser cache / Visitor analysis in the browser cache
• density-based cluster
  • about / Customer categorization analysis of e-commerce and DBSCAN
• density-based methods
  • about / Intrusion detection and density-based methods
  • OPTICS-OF algorithm / The OPTICS-OF algorithm
  • High Contrast Subspace (HiCS) algorithm / The High Contrast Subspace algorithm
  • R implementation / The R implementation
  • intrusion detection / Intrusion detection
• directed graphs
  • about / Graph
• discrete, numeric attributes
  • about / Numeric attributes
• discriminative frequent pattern-based classification
  • about / Discriminative frequent pattern-based classification
  • pseudocode / Discriminative frequent pattern-based classification
• disjunctive normal form (DNF)
  • about / Web sentiment analysis and CLIQUE
• distance-based outlier detection algorithm
  • about / The distance-based algorithm
• divisive clustering
  • about / News categorization and hierarchical clustering
• document retrieval
  • with SVM algorithm / Document retrieval
• document text
  • automatic abstraction, k-medoids algorithm used / Automatic abstraction and summarization of document text
• Dolphin algorithm
  • about / The Dolphin algorithm

E

• e-commerce
  • customer categorization analysis / Customer categorization analysis of e-commerce
• Eclat algorithm
  • about / The Eclat algorithm
  • R implementation / The R implementation
• eigenvalues
  • about / Eigenvalues and Eigenvectors
• eigenvectors
  • about / Eigenvalues and Eigenvectors
• EM methods
  • about / Ensemble (EM) methods
  • structure / Ensemble (EM) methods
  • bagging algorithm / The bagging algorithm
  • AdaBoost algorithm / The boosting and AdaBoost algorithms
  • boosting algorithm / The boosting and AdaBoost algorithms
  • Random forests algorithm / The Random forests algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
• escription length (DL)
  • about / The RIPPER algorithm
• Expectation Maximization (EM) algorithm
  • about / User search intent and the EM algorithm
  • pseudocode / The EM algorithm
  • R implementation / The R implementation
  • user search intent, determining / The user search intent

F

• FCA-based association rule mining algorithm
  • used, for web usage mining / The FCA-based association rule mining algorithm
  • R implementation / The R implementation
• feature extraction, examples
  • frequent itemsets / Feature extraction
  • similar items / Feature extraction
• FindAllOutsD algorithm
  • about / The FindAllOutsD algorithm
• FindAllOutsM algorithm
  • about / The FindAllOutsM algorithm
• FP-growth algorithm
  • about / The FP-growth algorithm
  • input data characteristics / Input data characteristics and data structure
  • data structure / Input data characteristics and data structure
  • pseudo code / The FP-growth algorithm
  • R implementation / The R implementation
• frequent itemset
  • about / The frequent itemset
• Frequent Itemset Mining Dataset Repository
  • URL / Data source
  • about / Data source
• frequent patterns
  • about / Patterns and pattern discovery
  • frequent itemset / Patterns and pattern discovery, The frequent itemset
  • frequent substructures / Patterns and pattern discovery, The frequent substructures
  • frequent subsequence / Patterns and pattern discovery, The frequent subsequence
• frequent subgraph patterns mining algorithm
  • about / Mining frequent subgraph patterns
  • gPLS algorithm / Mining frequent subgraph patterns
  • GraphSig algorithm / The gSpan algorithm
  • gSpan algorithm / The gSpan algorithm
  • R implementation / The R implementation
• frequent subsequence
  • about / The frequent subsequence
  • examples / The frequent subsequence
• frequent substructures
  • about / The frequent substructures
  • examples / The frequent substructures
• future prices
  • predicting / Predicting future prices

G

• GenMax algorithm
  • about / The GenMax algorithm with maximal frequent itemsets
  • R implementation / The R implementation
• genre categorization
  • of web pages / Genre categorization of web pages
• graph
  • about / Graph
  • directed graphs / Graph
  • undirected graphs / Graph
• Graph-Based Sub-topic Partition Algorithm (GSPSummary) algorithm
  • about / The multidocument summarization algorithm
• graph and network data
  • clustering / SNS and clustering graph and network data
• graph mining
  • about / Graph mining
  • algorithms / Graph mining algorithms
• GSP algorithm
  • sequence dataset, mining / The GSP algorithm
  • features / The GSP algorithm
  • R implementation / The R implementation

H

• hError algorithm
  • about / The hError algorithm
  • R implementation / The R implementation
• hierarchical clustering
  • about / News categorization and hierarchical clustering
  • agglomerative clustering / News categorization and hierarchical clustering
  • divisive clustering / News categorization and hierarchical clustering
  • characteristics / News categorization and hierarchical clustering
  • BIRCH algorithm / The BIRCH algorithm
  • chameleon algorithm / The chameleon algorithm
  • Bayesian hierarchical clustering algorithm / The Bayesian hierarchical clustering algorithm
  • probabilistic hierarchical clustering algorithm / The probabilistic hierarchical clustering algorithm
  • R implementation / The R implementation
  • used, for news categorization / News categorization
• hierarchical clustering algorithm
  • about / Hierarchical clustering to detect outliers
• high-dimensional data
  • clustering / Customer purchase data analysis and clustering high-dimensional data
• high-performance algorithms
  • about / High-performance algorithms
• high-value credit card customers
  • classifying, ID3 algorithm used / High-value credit card customers classification using ID3, High-value credit card customers classification
• High Contrast Subspace (HiCS) algorithm
  • about / The High Contrast Subspace algorithm
• HilOut algorithm
  • about / The HilOut algorithm
  • R implementation / The R implementation
• horizontal format
  • about / Input data characteristics and data structure
• hybrid association rules mining
  • about / Hybrid association rules mining
  • multilevel and multidimensional association rules mining / Hybrid association rules mining, Mining multilevel and multidimensional association rules
  • constraint-based frequent pattern mining / Hybrid association rules mining, Constraint-based frequent pattern mining

I

• ID3 algorithm
  • about / High-value credit card customers classification using ID3
  • used, for classifying high-value credit card customers / High-value credit card customers classification using ID3, High-value credit card customers classification
  • input parameters / The ID3 algorithm
  • output parameter / The ID3 algorithm
  • pseudocode / The ID3 algorithm
  • R implementation / The R implementation
  • used, for web attack detection / Web attack detection
• interval-scaled
  • dissimilarity / Data measuring
• intrusion detection
  • about / Intrusion detection
• Intrusion Detection System (IDS)
  • about / Web attack detection
• IR
  • about / Information retrieval and text mining
• iterative classification algorithms
  • about / The node classification and iterative classification algorithms

K

• k-itemset
  • about / The frequent itemset
• k-means algorithm
  • about / Search engines and the k-means algorithm, The k-means-based algorithm
  • search engine / Search engines and the k-means algorithm, Search engine and web page clustering
  • shortages / Search engines and the k-means algorithm
  • guidelines / Search engines and the k-means algorithm
  • pseudocode / The k-means clustering algorithm
  • kernel k-means algorithm, pseudocode / The kernel k-means algorithm
  • k-modes algorithm / The k-modes algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
• k-medoids algorithm
  • about / Automatic abstraction of document texts and the k-medoids algorithm
  • case considerations / Automatic abstraction of document texts and the k-medoids algorithm
  • PAM algorithm / The PAM algorithm
  • R implementation / The R implementation
  • used, for automatic abstraction of document text / Automatic abstraction and summarization of document text
• kNN algorithm
  • about / Protein classification and the k-Nearest Neighbors algorithm, The kNN algorithm
  • used, for protein classification / Protein classification and the k-Nearest Neighbors algorithm
  • pseudocode / The kNN algorithm
  • R implementation / The R implementation

L

• likelihood-based outlier detection algorithm
  • about / The likelihood-based outlier detection algorithm
  • R implementation / The R implementation
• Local Outlier Factor (LOF)
  • about / Intrusion detection and density-based methods
• Local Reachability Density (LRD)
  • about / Intrusion detection and density-based methods

M

• machine learning
  • statistics / Statistics and machine learning
• machine learning (ML)
  • about / Web data mining, Machine learning
  • architecture / Machine learning architecture
  • training and testing / Machine learning architecture
  • batch versus online learning / Machine learning architecture
  • feature selection / Machine learning architecture
  • training set, creating / Machine learning architecture
• machine learning (ML), classes
  • decision tree / Approaches to machine learning
  • perceptron / Approaches to machine learning
  • neural nets / Approaches to machine learning
  • instance-based learning / Approaches to machine learning
  • support-vector machines / Approaches to machine learning
• MAFIA algorithm
  • about / Customer purchase data analysis and clustering high-dimensional data
  • pseudocode / The MAFIA algorithm
  • customer purchase data analysis / Customer purchase data analysis
• MapReduce
  • C4.5 algorithm, parallel version / A parallel version with MapReduce
  • EM methods, parallel version / Parallel version with MapReduce
  • SVM algorithm, parallel version / Parallel version with MapReduce
  • BP algorithm, parallel version / Parallel version with MapReduce
  • k-means algorithm, parallel version / Parallel version with MapReduce
• market basket analysis
  • about / Market basket analysis
  • market basket model / The market basket model
  • A-Priori algorithm / A-Priori algorithms
  • Eclat algorithm / The Eclat algorithm
  • FP-growth algorithm / The FP-growth algorithm
  • GenMax algorithm / The GenMax algorithm with maximal frequent itemsets
  • Charm algorithm / The Charm algorithm with closed frequent itemsets
  • association rules, generating / The algorithm to generate association rules
• market basket model
  • about / The market basket model
• maximal frequent itemset (MFI)
  • about / The GenMax algorithm with maximal frequent itemsets
  • mining, with GenMax algorithm / The GenMax algorithm with maximal frequent itemsets
• Maximal Marginal Relevance (MMR) algorithm
  • about / The Maximal Marginal Relevance algorithm
  • R implementation / The R implementation
• Maximum Likelihood Estimation (MLE)
  • about / User search intent and the EM algorithm
• missing values
  • avoiding / Missing values
  • considerations / Missing values
• mobile fraud detection
  • about / Activity monitoring and the detection of mobile fraud
• multidocument summarization algorithm
  • about / The multidocument summarization algorithm
• multilevel and multidimensional association rules mining
  • about / Mining multilevel and multidimensional association rules

N

• 1NN classifier algorithm
  • about / Time-series classification with the 1NN classifier
• N-gram-based text-categorization algorithm
  • used, for categorizing newspaper articles / Categorizing newspaper articles and newswires into topics
  • used, for categorizing newswires / Categorizing newspaper articles and newswires into topics
  • about / The N-gram-based text categorization
  • pseudocode / The N-gram-based text categorization
  • R implementation / The R implementation
• Naïve Bayes classification
  • used, for identifying spam e-mail / Identify spam e-mail and Naïve Bayes classification, Identify spam e-mail
  • characteristics / Identify spam e-mail and Naïve Bayes classification
  • pseudocode / The Naïve Bayes classification
  • R implementation / The R implementation
• news categorization
  • with hierarchical clustering / News categorization
• NL algorithm
  • about / The NL algorithm
• nominal attributes
  • dissimilarity / Data measuring
• normalization methods, data transformation
  • min-max normalization / Normalization data transformation methods
  • z-score normalization / Normalization data transformation methods
  • normalization by decimal scaling / Normalization data transformation methods
• numeric attributes
  • about / Numeric attributes
• numeric attributes, types
  • interval-scaled / Numeric attributes
  • ratio-scaled / Numeric attributes

O

• OCSVM (One Class SVM) algorithm
  • about / The OCSVM algorithm
• ODIN algorithm
  • about / The ODIN algorithm
• one-class nearest neighbor algorithm
  • about / The one-class nearest neighbor algorithm
• opinion-orientation algorithm
  • about / Opinion mining
• opinion mining
  • about / Opinion mining
• OPTICS-OF algorithm
  • about / The OPTICS-OF algorithm
• OPTICS algorithm
  • about / Clustering web pages and OPTICS, The OPTICS algorithm
  • core-distance of object / Clustering web pages and OPTICS
  • reachability-distance of object / Clustering web pages and OPTICS
  • pseudocode / The OPTICS algorithm
  • R implementation / The R implementation
  • web pages, clustering / Clustering web pages
• ordinal attributes
  • dissimilarity / Data measuring
• outlier detection
  • with statistical method / Credit card fraud detection and statistical methods
  • proximity-based methods / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
  • density-based methods / Intrusion detection and density-based methods
  • clustering-based methods / Intrusion detection and clustering-based methods
  • classification-based methods / Monitoring the performance of the web server and classification-based methods
  • topic detection / Detecting novelty in text and topic detection
  • novelty, detecting in text / Detecting novelty in text and topic detection
  • in high-dimensional data / Outlier detection in high-dimensional data
  • brute-force algorithm / The brute-force algorithm
  • HilOut algorithm / The HilOut algorithm

P

• PAM algorithm
  • about / The PAM algorithm
• partition-based clustering
  • about / Search engines and the k-means algorithm
  • characteristics / Search engines and the k-means algorithm
• patterns
  • about / An overview of associations and patterns
  • frequent patterns / Patterns and pattern discovery
• PCA
  • about / Principal-Component Analysis
• PrefixSpan algorithm
  • about / The PrefixSpan algorithm
  • R implementation / The R implementation
• probabilistic hierarchical clustering algorithm
  • about / The probabilistic hierarchical clustering algorithm
• process, data mining
  • CRISP-DM / The data mining process, CRISP-DM
  • SEMMA / The data mining process, SEMMA
• proximity-based methods
  • about / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
  • density-based outlier detection algorithm / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods
  • distance-based outlier detection algorithm / Activity monitoring – the detection of fraud involving mobile phones and proximity-based methods, The distance-based algorithm
  • NL algorithm / The NL algorithm
  • FindAllOutsM algorithm / The FindAllOutsM algorithm
  • FindAllOutsD algorithm / The FindAllOutsD algorithm
  • Dolphin algorithm / The Dolphin algorithm
  • R implementation / The R implementation
  • activity monitoring / Activity monitoring and the detection of mobile fraud
  • mobile fraud detection / Activity monitoring and the detection of mobile fraud

Q

• queries
  • keyword query / Information retrieval and text mining
  • boolean query / Information retrieval and text mining
  • phrase query / Information retrieval and text mining
  • proximity query / Information retrieval and text mining
  • full document query / Information retrieval and text mining
  • natural language questions / Information retrieval and text mining
• question answering (QA) system
  • about / The question answering system

R

• R
  • about / Why R?
  • advantage / Why R?
  • disadvantage / What are the disadvantages of R?
  • statistics / Statistics and R
  • visualization / Visualization with R
• Random forests algorithm
  • about / The Random forests algorithm
• recommendation systems
  • about / Recommendation systems
• Relative Closeness (RC), chameleon algorithm
  • about / The chameleon algorithm
• Relative Interconnectivity (RI), chameleon algorithm
  • about / The chameleon algorithm
• RHadoop
  • about / Big data
• RIPPER algorithm
  • about / The RIPPER algorithm
  • pseudocode / The RIPPER algorithm
• route outlier detection (ROD) algorithm
  • about / The route outlier detection (ROD) algorithm
  • R implementation / The R implementation
• rule-based classification
  • about / Rule-based classification of player types in computer games and rule-based classification, Rule-based classification
  • decision tree, transforming into decision rules / Transformation from decision tree to decision rules
  • sequential covering algorithm / Sequential covering algorithm
  • RIPPER algorithm / The RIPPER algorithm
  • R implementation / The R implementation
  • player types, classifying in computer games / Rule-based classification of player types in computer games
• rules
  • association rules / Relationship or rules discovery, Association rules
  • correlation rules / Relationship or rules discovery, Correlation rules
  • generating, from sequential patterns / Rule generation from sequential patterns

S

• search engine
  • web page clustering / Search engine and web page clustering
• SEMMA
  • about / The data mining process, SEMMA
  • sample / SEMMA
  • explore / SEMMA
  • modify / SEMMA
  • model / SEMMA
  • assess / SEMMA
• sentential frequent itemsets
  • used, for text classification / Text classification using sentential frequent itemsets
• sequence dataset
  • mining / Mining sequence dataset
  • about / Sequence dataset
  • mining, with GSP algorithm / The GSP algorithm
• sequence patterns
  • mining / Mining sequence patterns in transactional databases
  • PrefixSpan algorithm / The PrefixSpan algorithm
• sequential covering algorithm
  • about / Sequential covering algorithm
  • pseudocode / Sequential covering algorithm
• sequential patterns
  • rules, generating / Rule generation from sequential patterns
• shingling algorithm
  • about / Social network mining
• single-pass-any-time clustering algorithm
  • about / The single-pass-any-time clustering algorithm
• social network
  • mining / Social network mining
  • characteristics / Social network
  • telephone networks / Social network
  • e-mail networks / Social network
  • collaboration networks / Social network
  • example / Social network
• social networking service (SNS)
  • about / Social networking service (SNS)
• social network mining
  • about / Social network mining
  • community detection / Social network mining
  • shingling algorithm / Social network mining
  • node classification / The node classification and iterative classification algorithms
  • iterative classification algorithms / The node classification and iterative classification algorithms
  • R implementation / The R implementation
• SPADE algorithm
  • about / The SPADE algorithm
  • features / The SPADE algorithm
  • R implementation / The R implementation
• spam e-mail
  • identifying, Naïve Bayes classification used / Identify spam e-mail and Naïve Bayes classification, Identify spam e-mail
• spectral clustering algorithm
  • about / The spectral clustering algorithm
  • pseudocode / The spectral clustering algorithm
  • R implementation / The R implementation
• squared error-based clustering algorithm
  • about / Search engines and the k-means algorithm
• statistical method
  • about / Credit card fraud detection and statistical methods
  • likelihood-based outlier detection algorithm / The likelihood-based outlier detection algorithm
  • credit card fraud detection / Credit card fraud detection
• statistics
  • about / Statistics
  • data mining / Statistics and data mining
  • machine learning / Statistics and machine learning
  • and R / Statistics and R
  • limitations, on data mining / The limitations of statistics on data mining
• STING algorithm
  • about / Recommendation system and STING
  • characteristics / Recommendation system and STING
  • pseudocode / The STING algorithm
  • R implementation / The R implementation
  • recommendation systems / Recommendation systems
• stock market data
  • about / Stock market data
• STREAM algorithm
  • about / The credit card transaction flow and STREAM algorithm
  • pseudocode / The STREAM algorithm
  • R implementation / The R implementation
  • credit card transaction flow / The credit card transaction flow
• stream data
  • mining / The credit card transaction flow and STREAM algorithm
• Structural Clustering Algorithm for Network (SCAN) algorithm
  • about / SNS and clustering graph and network data
  • pseudocode / The SCAN algorithm
  • R implementation / The R implementation
  • social networking service (SNS) / Social networking service (SNS)
• summarization
  • about / Summarization
• SURFING algorithm
  • pseudocode / The SURFING algorithm
  • about / The SURFING algorithm
  • R implementation / The R implementation
• SVD
  • about / Singular-value decomposition
• SVM algorithm
  • about / Document retrieval and Support Vector Machine
  • pseudocode / The SVM algorithm
  • R implementation / The R implementation
  • parallel version, with MapReduce / Parallel version with MapReduce
  • used, for document retrieval / Document retrieval
• symbolic sequences
  • mining / Web click streams and mining symbolic sequences

T

• Term Frequency-Inverse Document Frequency (TF-IDF)
  • about / Search engine and web page clustering
• text classification
  • with sentential frequent itemsets / Text classification using sentential frequent itemsets
• text mining
  • about / Text mining, Text mining and TM packages
  • IR / Information retrieval and text mining
  • for prediction / Mining text for prediction
• Text Retrieval Conference (TREC)
  • about / Identify spam e-mail
• text summarization
  • about / Text summarization
  • topic representation / Topic representation
  • multidocument summarization algorithm / The multidocument summarization algorithm
  • Maximal Marginal Relevance (MMR) algorithm / The Maximal Marginal Relevance algorithm
• time-series data
  • mining / Predicting future prices and time-series analysis
  • clustering / Stock market data and time-series clustering and classification
  • clustering, with hError algorithm / The hError algorithm
  • clustering, with 1NN classifier algorithm / Time-series classification with the 1NN classifier
  • stock market data / Stock market data
• Time To Live (TTL)
  • about / Trojan traffic identification method
• topic detection
  • about / Detecting novelty in text and topic detection
• topic representation
  • about / Topic representation
• topic signature
  • about / Topic representation
• Tracking Evolving Clusters in NOisy Streams (TECNO-STREAMS) algorithm
  • about / Web click streams and mining symbolic sequences, The TECNO-STREAMS algorithm
  • R implementation / The R implementation
  • used, for mining web click streams / Web click streams
• tree pruning
  • about / Tree pruning
  • post-pruning / Tree pruning
  • pre-pruning / Tree pruning
• Trojan horse
  • about / Trojan traffic identification method
• Trojan traffic identification
  • with Bayes classification / Trojan traffic identification method and Bayes classification, Trojan traffic identification method

U

• UCI Machine Learning Repository
  • about / Data source
  • URL / Data source
• undirected graphs
  • about / Graph
• unsupervised image categorization
  • with affinity propagation (AP) clustering / Unsupervised image categorization
• user search intent
  • determining / The user search intent

V

• vector-space model
  • about / Search engine and web page clustering
• vertical format
  • about / Input data characteristics and data structure
• visitor analysis, in browser cache
  • hit / Visitor analysis in the browser cache
  • unique visitors / Visitor analysis in the browser cache
  • new/return visitors / Visitor analysis in the browser cache
  • page views / Visitor analysis in the browser cache
  • page views per visitor / Visitor analysis in the browser cache
  • IP address / Visitor analysis in the browser cache
  • visitor location / Visitor analysis in the browser cache
  • visitor language / Visitor analysis in the browser cache
  • referring pages/sites (URLs) / Visitor analysis in the browser cache
  • keywords / Visitor analysis in the browser cache
  • browser type / Visitor analysis in the browser cache
  • operating system version / Visitor analysis in the browser cache
  • screen resolution / Visitor analysis in the browser cache
  • Java or Flash-enabled / Visitor analysis in the browser cache
  • connection speed / Visitor analysis in the browser cache
  • errors / Visitor analysis in the browser cache
  • visit duration / Visitor analysis in the browser cache
  • visitor paths/navigation / Visitor analysis in the browser cache
  • bounce rate / Visitor analysis in the browser cache
• visualization
  • about / Visualization of results
  • with R / Visualization with R
• visualization, features
  • novel / Visualization of results
  • informative / Visualization of results
  • efficient / Visualization of results
  • aesthetic / Visualization of results

W

• WAVE clustering algorithm
  • about / Opinion mining and WAVE clustering
  • characteristics / Opinion mining and WAVE clustering
  • pseudocode / The WAVE cluster algorithm
  • R implementation / The R implementation
  • opinion mining / Opinion mining
• web attack
  • detecting, ID3 algorithm used / Web attack detection
  • DOS / Web attack detection
  • R2L / Web attack detection
  • U2R / Web attack detection
  • probing / Web attack detection
• web click streams
  • mining / Web click streams and mining symbolic sequences, Web click streams
• web data mining
  • about / Web data mining
  • web structure mining / Web data mining
  • web content mining / Web data mining
  • web usage mining / Web data mining
• web data mining, tasks
  • information extraction (IE) / Web data mining
  • natural language processing (NLP) / Web data mining
  • question answering / Web data mining
  • resource discovery / Web data mining
• web key resource page judgment
  • with CART algorithm / Web key resource page judgment using CART, Web key resource page judgment
  • attributes / Web key resource page judgment
• web logs
  • used, for web usage mining / Web usage mining with web logs
• web page clustering
  • about / Search engine and web page clustering
• web pages
  • clustering / Clustering web pages
  • genre categorization / Genre categorization of web pages
• web sentiment analysis
  • about / Web sentiment analysis
• web server
  • performance, monitoring / Monitoring the performance of the web server
• web spam
  • detecting, C4.5 algorithm used / Web spam detection using C4.5, Web spam detection
  • link spam / Web spam detection
  • content spam / Web spam detection
  • cloaking / Web spam detection
• web usage mining
  • with web logs / Web usage mining with web logs
  • with, FCA-based association rule mining algorithm / The FCA-based association rule mining algorithm
• WordNet
  • URL / Data source