Data Classification

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Book description

Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, this book explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. It presents core methods in data classification, covers recent problem domains, and discusses advanced methods for enhancing the quality of the underlying classification results.

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Preliminaries
Series
Dedication
Editor Biography
Contributors
Preface
Chapter 1 An Introduction to Data Classification
1. 1.1 Introduction
2. 1.2 Common Techniques in Data Classification
  1. 1.2.1 Feature Selection Methods
  2. 1.2.2 Probabilistic Methods
  3. 1.2.3 Decision Trees
  4. 1.2.4 Rule-Based Methods
  5. 1.2.5 Instance-Based Learning
  6. 1.2.6 SVM Classifiers
  7. 1.2.7 Neural Networks
  1. 1.3.1 Large Scale Data: Big Data and Data Streams
    1. 1.3.1.1 Data Streams
    2. 1.3.1.2 The Big Data Framework
    1. 1.4.1 Rare Class Learning
    2. 1.4.2 Distance Function Learning
    3. 1.4.3 Ensemble Learning for Data Classification
    4. 1.4.4 Enhancing Classification Methods with Additional Data
      1. 1.4.4.1 Semi-Supervised Learning
      2. 1.4.4.2 Transfer Learning
      1. 1.4.5.1 Active Learning
      2. 1.4.5.2 Visual Learning
      1. Figure 1.1
      2. Figure 1.2
      3. Figure 1.3
      4. Figure 1.4
      5. Figure 1.5
      1. Table 1.1
      1. 2.1 Introduction
        
        2.1.1 Data Classification
        
        2.1.2 Feature Selection
        
        2.1.3 Feature Selection for Classification
        
        2.2.1 Filter Models
        
        2.2.2 Wrapper Models
        
        2.2.3 Embedded Models
        
        2.3.1 Features with Group Structure
        
        2.3.2 Features with Tree Structure
        
        2.3.3 Features with Graph Structure
        
        2.4.1 The Grafting Algorithm
        
        2.4.2 The Alpha-Investing Algorithm
        
        2.4.3 The Online Streaming Feature Selection Algorithm
        
        2.5.1 Scalability
        
        2.5.2 Stability
        
        2.5.3 Linked Data
        
        Figure 2.1
        
        Figure 2.2
        
        Figure 2.3
        
        Figure 2.4
        
        Figure 2.5
        
        Figure 2.6
        
        Figure 2.7
        
        Figure 2.8
        
        Figure 2.9
        
        Figure 2.10
        
        3.1 Introduction
        
        3.2 Naive Bayes Classification
        
        3.2.1 Bayes’ Theorem and Preliminary
        
        3.2.2 Naive Bayes Classifier
        
        3.2.3 Maximum-Likelihood Estimates for Naive Bayes Models
        
        3.2.4 Applications
        
        3.3.1 Logistic Regression
        
        3.3.2 Parameters Estimation for Logistic Regression
        
        3.3.3 Regularization in Logistic Regression
        
        3.3.4 Applications
        
        3.4.1 Bayesian Networks
        
        3.4.1.1 Bayesian Network Construction
        
        3.4.1.2 Inference in a Bayesian Network
        
        3.4.1.3 Learning Bayesian Networks
        
        3.4.2.1 The Inference and Learning Algorithms
        
        3.4.3.1 Conditional Independence
        
        3.4.3.2 Clique Factorization
        
        3.4.3.3 The Inference and Learning Algorithms
        
        3.4.4.1 The Learning Algorithms
        
        Figure 3.1
        
        Figure 3.2
        
        Figure 3.3
        
        Figure 3.4
        
        4.1 Introduction
        
        4.2 Top-Down Decision Tree Induction
        
        4.2.1 Node Splitting
        
        4.2.2 Tree Pruning
        
        4.3.1 Splitting Criteria
        
        4.3.2 Stopping Conditions
        
        4.3.3 Pruning Strategy
        
        4.3.4 Handling Unknown Values: Induction and Prediction
        
        4.3.5 Other Issues: Windowing and Multivariate Criteria
        
        4.4.1 RainForest-Based Approach
        
        4.4.2 SPIES Approach
        
        4.4.3 Parallel Decision Tree Construction
        
        4.5.1 ID3 Family
        
        4.5.2 VFDT Family
        
        4.5.3 Ensemble Method for Streaming Data
        
        Figure 4.1
        
        Figure 4.2
        
        Figure 4.3
        
        Figure 4.4
        
        Figure 4.5
        
        Figure 4.6
        
        Table 4.1
        
        Table 4.2
        
        Table 4.3
        
        5.1 Introduction
        
        5.2 Rule Induction
        
        5.2.1 Two Algorithms for Rule Induction
        
        5.2.1.1 CN2 Induction Algorithm (Ordered Rules)
        
        5.2.1.2 RIPPER Algorithm and Its Variations (Ordered Classes)
        
        5.3.1 Association Rule Mining
        
        5.3.1.1 Definitions of Association Rules, Support, and Confidence
        
        5.3.1.2 The Introduction of Apriori Algorithm
        
        5.3.3.1 Additional Discussion for CARs Mining
        
        5.3.3.2 Building a Classifier Using CARs
        
        5.4.1 Text Categorization
        
        5.4.2 Intrusion Detection
        
        5.4.3 Using Class Association Rules for Diagnostic Data Mining
        
        5.4.4 Gene Expression Data Analysis
        
        Table 5.1
        
        Table 5.2
        
        Table 5.3
        
        6.1 Introduction
        
        6.2 Instance-Based Learning Framework
        
        6.3 The Nearest Neighbor Classifier
        
        6.3.1 Handling Symbolic Attributes
        
        6.3.2 Distance-Weighted Nearest Neighbor Methods
        
        6.3.3 Local Distance Scaling
        
        6.3.4 Attribute-Weighted Nearest Neighbor Methods
        
        6.3.5 Locally Adaptive Nearest Neighbor Classifier
        
        6.3.6 Combining with Ensemble Methods
        
        6.3.7 Multi-Label Learning
        
        Figure 6.1
        
        Figure 6.2
        
        Figure 6.3
        
        Figure 6.4
        
        Figure 6.5
        
        7.1 Introduction
        
        7.2 The Maximum Margin Perspective
        
        7.3 The Regularization Perspective
        
        7.4 The Support Vector Perspective
        
        7.5 Kernel Tricks
        
        7.6 Solvers and Algorithms
        
        7.7 Multiclass Strategies
        
        7.8 Conclusion
        
        Bibliography
        
        Figure 7.1
        
        Figure 7.2
        
        Figure 7.3
        
        Figure 7.4
        
        Figure 7.5
        
        Figure 7.6
        
        Figure 7.7
        
        Figure 7.8
        
        Figure 7.9
        
        8.1 Introduction
        
        8.2 Fundamental Concepts
        
        8.2.1 Mathematical Model of a Neuron
        
        8.2.2 Types of Units
        
        8.2.2.1 McCullough Pitts Binary Threshold Unit
        
        8.2.2.2 Linear Unit
        
        8.2.2.3 Linear Threshold Unit
        
        8.2.2.4 Sigmoidal Unit
        
        8.2.2.5 Distance Unit
        
        8.2.2.6 Radial Basis Unit
        
        8.2.2.7 Polynomial Unit
        
        8.2.3.1 Layered Network
        
        8.2.3.2 Networks with Feedback
        
        8.2.3.3 Modular Networks
        
        8.2.5.1 Hebbian Rule
        
        8.2.5.2 The Delta Rule
        
        8.3.1 The Single-Layer Perceptron
        
        8.3.1.1 Perceptron Criterion
        
        8.3.1.2 Multi-Class Perceptrons
        
        8.3.1.3 Perceptron Enhancements
        
        8.3.2.1 Two-Class Adaline
        
        8.3.2.2 Multi-Class Adaline
        
        8.3.3.1 LVQ1 Training
        
        8.3.3.2 LVQ2 Training
        
        8.3.3.3 Application and Limitations
        
        8.4.1 Radial Basis Function Network
        
        8.4.2 RBFN Training
        
        8.4.2.1 Using Training Samples as Centers
        
        8.4.2.2 Random Selection of Centers
        
        8.4.2.3 Unsupervised Selection of Centers
        
        8.4.2.4 Supervised Estimation of Centers
        
        8.4.2.5 Linear Optimization of Weights
        
        8.4.2.6 Gradient Descent and Enhancements
        
        8.5.1 MLP Architecture for Classification
        
        8.5.1.1 Two-Class Problems
        
        8.5.1.2 Multi-Class Problems
        
        8.5.1.3 Forward Propagation
        
        8.5.2.1 Mean Square Error (MSE)
        
        8.5.2.2 Cross-Entropy (CE)
        
        8.5.2.3 Minimum Classification Error (MCE)
        
        8.5.4.1 Backpropagation with Momentum
        
        8.5.4.2 Delta-Bar-Delta
        
        8.5.4.3 Rprop Algorithm
        
        8.5.4.4 Quick-Prop
        
        8.6.1 Use of Prior Knowledge
        
        8.6.2 Layer-Wise Greedy Training
        
        8.6.2.1 Deep Belief Networks (DBNs)
        
        8.6.2.2 Stack Auto-Encoder
        
        Figure 8.1
        
        Figure 8.2
        
        Figure 8.3
        
        Figure 8.4
        
        Table 8.1
        
        9.1 Introduction
        
        9.2 Generic Stream Classification Algorithms
        
        9.2.1 Decision Trees for Data Streams
        
        9.2.2 Rule-Based Methods for Data Streams
        
        9.2.3 Nearest Neighbor Methods for Data Streams
        
        9.2.4 SVM Methods for Data Streams
        
        9.2.5 Neural Network Classifiers for Data Streams
        
        9.2.6 Ensemble Methods for Data Streams
        
        9.3.1 Detecting Rare Classes
        
        9.3.2 Detecting Novel Classes
        
        9.3.3 Detecting Infrequently Recurring Classes
        
        9.5.1 Text Streams
        
        9.5.2 Graph Streams
        
        9.5.3 Uncertain Data Streams
        
        Figure 9.1
        
        Figure 9.2
        
        Table 9.1
        
        10.1 Introduction
        
        10.2 Scale-Up on a Single Machine
        
        10.2.1 Background
        
        10.2.2 SVMPerf
        
        10.2.3 Pegasos
        
        10.2.4 Bundle Methods
        
        10.3.1 Parallel Decision Trees
        
        10.3.2 Parallel SVMs
        
        10.3.3 MRM-ML
        
        10.3.4 SystemML
        
        11.1 Introduction
        
        11.2 Feature Selection for Text Classification
        
        11.2.1 Gini Index
        
        11.2.2 Information Gain
        
        11.2.3 Mutual Information
        
        11.2.4 χ2-Statistic
        
        11.2.5 Feature Transformation Methods: Unsupervised and Supervised LSI
        
        11.2.6 Supervised Clustering for Dimensionality Reduction
        
        11.2.7 Linear Discriminant Analysis
        
        11.2.8 Generalized Singular Value Decomposition
        
        11.2.9 Interaction of Feature Selection with Classification
        
        11.5.1 Bernoulli Multivariate Model
        
        11.5.2 Multinomial Distribution
        
        11.5.3 Mixture Modeling for Text Classification
        
        11.6.1 SVM Classifiers
        
        11.6.2 Regression-Based Classifiers
        
        11.6.3 Neural Network Classifiers
        
        11.6.4 Some Observations about Linear Classifiers
        
        11.9.1 Classifier Ensemble Learning
        
        11.9.2 Data Centered Methods: Boosting and Bagging
        
        11.9.3 Optimizing Specific Measures of Accuracy
        
        11.10.1 Semi-Supervised Learning
        
        11.10.2 Transfer Learning
        
        11.10.3 Active Learning
        
        Figure 11.1
        
        Figure 11.2
        
        Figure 11.3
        
        Figure 11.4
        
        12.1 Introduction
        
        12.1.1 Overview
        
        12.2.1 Text Features
        
        12.2.2 Image Features
        
        12.2.3 Audio Features
        
        12.2.4 Video Features
        
        12.3.1 Fusion Methods
        
        12.3.2 Audio Visual Speech Recognition
        
        12.3.2.1 Visual Front End
        
        12.3.2.2 Decision Fusion on HMM
        
        12.4.1 Popular Applied Ontology
        
        12.4.2 Ontological Relations
        
        12.4.2.1 Definition
        
        12.4.2.2 Subclass Relation
        
        12.4.2.3 Co-Occurrence Relation
        
        12.4.2.4 Combination of the Two Relations
        
        12.4.2.5 Inherently Used Ontology
        
        12.5.1 Data Modalities
        
        12.5.2 Challenges in Geographical Classification
        
        12.5.3 Geo-Classification for Images
        
        12.5.3.1 Classifiers
        
        Figure 12.1
        
        Figure 12.2
        
        Figure 12.3
        
        Figure 12.4
        
        Figure 12.5
        
        Figure 12.6
        
        Figure 12.7
        
        Figure 12.8
        
        Figure 12.9
        
        13.1 Introduction
        
        13.2 Time Series Representation
        
        13.3 Distance Measures
        
        13.3.1 Lp-Norms
        
        13.3.2 Dynamic Time Warping (DTW)
        
        13.3.3 Edit Distance
        
        13.3.4 Longest Common Subsequence (LCSS)
        
        13.4.1 Speeding up the k-NN
        
        14.1 Introduction
        
        14.2 Background
        
        14.2.1 Sequence
        
        14.2.2 Sequence Classification
        
        14.2.3 Frequent Sequential Patterns
        
        14.2.4 n-Grams
        
        14.4.1 Filtering Method for Sequential Feature Selection
        
        14.4.2 Pattern Mining Framework for Mining Sequential Features
        
        14.4.3 A Wrapper-Based Method for Mining Sequential Features
        
        14.5.0.1 Alignment-Based Distance
        
        14.5.0.2 Keyword-Based Distance
        
        14.5.0.3 Kernel-Based Similarity
        
        14.5.0.4 Model-Based Similarity
        
        14.5.0.5 Time Series Distance Metrics
        
        14.8.1 Semi-Supervised Sequence Classification
        
        14.8.2 Classification of Label Sequences
        
        14.8.3 Classification of Sequence of Vector Data
        
        15.1 Introduction
        
        15.2 Collective Classification Problem Definition
        
        15.2.1 Inductive vs. Transductive Learning
        
        15.2.2 Active Collective Classification
        
        15.3.1 Label Propagation
        
        15.3.2 Iterative Classification Algorithms
        
        15.5.1 Directed Models
        
        15.5.2 Undirected Models
        
        15.5.3 Approximate Inference in Graphical Models
        
        15.5.3.1 Gibbs Sampling
        
        15.5.3.2 Loopy Belief Propagation (LBP)
        
        15.6.1 Data Graph
        
        15.6.2 Relational Features
        
        Figure 15.1
        
        Figure 15.2
        
        16.1 Introduction
        
        16.2 Preliminaries
        
        16.2.1 Data Uncertainty Models
        
        16.2.2 Classification Framework
        
        16.3.1 Decision Trees
        
        16.3.2 Rule-Based Classification
        
        16.3.3 Associative Classification
        
        16.3.4 Density-Based Classification
        
        16.3.5 Nearest Neighbor-Based Classification
        
        16.3.6 Support Vector Classification
        
        16.3.7 Naive Bayes Classification
        
        Figure 16.1
        
        Figure 16.2
        
        Figure 16.3
        
        Figure 16.4
        
        Figure 16.5
        
        17.1 Introduction
        
        17.2 Rare Class Detection
        
        17.2.1 Cost Sensitive Learning
        
        17.2.1.1 MetaCost: A Relabeling Approach
        
        17.2.1.2 Weighting Methods
        
        17.2.1.3 Bayes Classifiers
        
        17.2.1.4 Proximity-Based Classifiers
        
        17.2.1.5 Rule-Based Classifiers
        
        17.2.1.6 Decision Trees
        
        17.2.1.7 SVM Classifier
        
        17.2.2.1 Relation between Weighting and Sampling
        
        17.2.2.2 Synthetic Over-Sampling: SMOTE
        
        17.2.2.3 One Class Learning with Positive Class
        
        17.2.2.4 Ensemble Techniques
        
        17.3.1 Difficult Cases and One-Class Learning
        
        17.4.1 One Class Novelty Detection
        
        17.4.2 Combining Novel Class Detection with Rare Class Detection
        
        17.4.3 Online Novelty Detection
        
        Figure 17.1
        
        Figure 17.2
        
        18.1 Introduction
        
        18.2 The Definition of Distance Metric Learning
        
        18.3 Supervised Distance Metric Learning Algorithms
        
        18.3.1 Linear Discriminant Analysis (LDA)
        
        18.3.2 Margin Maximizing Discriminant Analysis (MMDA)
        
        18.3.3 Learning with Side Information (LSI)
        
        18.3.4 Relevant Component Analysis (RCA)
        
        18.3.5 Information Theoretic Metric Learning (ITML)
        
        18.3.6 Neighborhood Component Analysis (NCA)
        
        18.3.7 Average Neighborhood Margin Maximization (ANMM)
        
        18.3.8 Large Margin Nearest Neighbor Classifier (LMNN)
        
        18.4.1 Semi-Supervised Metric Learning
        
        18.4.1.1 Laplacian Regularized Metric Learning (LRML)
        
        18.4.1.2 Constraint Margin Maximization (CMM)
        
        18.4.2.1 Pseudo-Metric Online Learning Algorithm (POLA)
        
        18.4.2.2 Online Information Theoretic Metric Learning (OITML)
        
        Table 18.1
        
        Table 18.2
        
        19.1 Introduction
        
        19.2 Bayesian Methods
        
        19.2.1 Bayes Optimal Classifier
        
        19.2.2 Bayesian Model Averaging
        
        19.2.3 Bayesian Model Combination
        
        19.3.1 General Idea
        
        19.3.2 Random Forest
        
        19.4.1 General Boosting Procedure
        
        19.4.2 AdaBoost
        
        19.5.1 General Stacking Procedure
        
        19.5.2 Stacking and Cross-Validation
        
        19.5.3 Discussions
        
        Figure 19.1
        
        Table 19.1
        
        Table 19.2
        
        Table 19.3
        
        Table 19.4
        
        Table 19.5
        
        Table 19.6
        
        Table 19.7
        
        Table 19.8
        
        Table 19.9
        
        Table 19.10
        
        Table 19.11
        
        Table 19.12
        
        Table 19.13
        
        Table 19.14
        
        Table 19.15
        
        20.1 Introduction
        
        20.1.1 Transductive vs. Inductive Semi-Supervised Learning
        
        20.1.2 Semi-Supervised Learning Framework and Assumptions
        
        20.2.1 Algorithms
        
        20.2.2 Description of a Representative Algorithm
        
        20.2.3 Theoretical Justification and Relevant Results
        
        20.3.1 Algorithms
        
        20.3.2 Description of a Representative Algorithm
        
        20.3.3 Theoretical Justification and Relevant Results
        
        20.4.1 Algorithms
        
        20.4.1.1 Graph Cut
        
        20.4.1.2 Graph Transduction
        
        20.4.1.3 Manifold Regularization
        
        20.4.1.4 Random Walk
        
        20.4.1.5 Large Scale Learning
        
        20.5.1 Algorithms
        
        20.5.2 Description of a Representative Algorithm
        
        20.5.3 Theoretical Justification and Relevant Results
        
        Figure 20.1
        
        Figure 20.2
        
        21.1 Introduction
        
        21.2 Transfer Learning Overview
        
        21.2.1 Background
        
        21.2.2 Notations and Definitions
        
        21.3.1 Instance-Based Approach
        
        21.3.1.1 Case I: No Target Labeled Data
        
        21.3.1.2 Case II: A Few Target Labeled Data
        
        21.3.2.1 Encoding Specific Knowledge for Feature Learning
        
        21.3.2.2 Learning Features by Minimizing Distance between Distributions
        
        21.3.2.3 Learning Features Inspired by Multi-Task Learning
        
        21.3.2.4 Learning Features Inspired by Self-Taught Learning
        
        21.3.2.5 Other Feature Learning Approaches
        
        21.4.1 Heterogeneous Feature Spaces
        
        21.4.2 Different Label Spaces
        
        21.6.1 Binary Classification vs. Multi-Class Classification
        
        21.6.2 Knowledge Transfer from Multiple Source Domains
        
        21.6.3 Transfer Learning Meets Active Learning
        
        21.7.1 NLP Applications
        
        21.7.2 Web-Based Applications
        
        21.7.3 Sensor-Based Applications
        
        21.7.4 Applications to Computer Vision
        
        21.7.5 Applications to Bioinformatics
        
        21.7.6 Other Applications
        
        Figure 21.1
        
        Table 21.1
        
        Table 21.2
        
        Table 21.3
        
        22.1 Introduction
        
        22.2 Motivation and Comparisons to Other Strategies
        
        22.2.1 Comparison with Other Forms of Human Feedback
        
        22.2.2 Comparisons with Semi-Supervised and Transfer Learning
        
        22.3.1 Heterogeneity-Based Models
        
        22.3.1.1 Uncertainty Sampling
        
        22.3.1.2 Query-by-Committee
        
        22.3.1.3 Expected Model Change
        
        22.3.2.1 Expected Error Reduction
        
        22.3.2.2 Expected Variance Reduction
        
        22.4.1 A Simple Example
        
        22.4.2 Existing Works
        
        22.4.3 Preliminaries
        
        22.4.4 Importance Weighted Active Learning
        
        22.4.4.1 Algorithm
        
        22.4.4.2 Consistency
        
        22.4.4.3 Label Complexity
        
        22.5.1 Active Learning in Sequences
        
        22.5.2 Active Learning in Graphs
        
        22.5.2.1 Classification of Many Small Graphs
        
        22.5.2.2 Node Classification in a Single Large Graph
        
        22.6.1 Active Learning of Features
        
        22.6.2 Active Learning of Kernels
        
        22.6.3 Active Learning of Classes
        
        22.6.4 Streaming Active Learning
        
        22.6.5 Multi-Instance Active Learning
        
        22.6.6 Multi-Label Active Learning
        
        22.6.7 Multi-Task Active Learning
        
        22.6.8 Multi-View Active Learning
        
        22.6.9 Multi-Oracle Active Learning
        
        22.6.10 Multi-Objective Active Learning
        
        22.6.11 Variable Labeling Costs
        
        22.6.12 Active Transfer Learning
        
        22.6.13 Active Reinforcement Learning
        
        Figure 22.1
        
        Figure 22.2
        
        23.1 Introduction
        
        23.1.1 Requirements for Visual Classification
        
        23.1.2 Visualization Metaphors
        
        23.1.2.1 2D and 3D Spaces
        
        23.1.2.2 More Complex Metaphors
        
        23.2.1 Nomograms
        
        23.2.1.1 Naïve Bayes Nomogram
        
        23.2.2.1 Edge Cluttering
        
        23.2.3.1 Star Coordinates
        
        23.2.4.1 Clustering
        
        23.2.4.2 Naïve Bayes Classification
        
        23.2.5.1 Self-Organizing Maps
        
        23.2.5.2 Generative Topographic Mapping
        
        23.2.6.1 Decision Trees
        
        23.2.6.2 Treemap
        
        23.2.6.3 Hyperbolic Tree
        
        23.2.6.4 Phylogenetic Trees
        
        23.3.1 EnsembleMatrix and ManiMatrix
        
        23.3.2 Systematic Mapping
        
        23.3.3 iVisClassifier
        
        23.3.4 ParallelTopics
        
        23.3.5 VisBricks
        
        23.3.6 WHIDE
        
        23.3.7 Text Document Retrieval
        
        Figure 23.1
        
        Figure 23.2
        
        Figure 23.3
        
        Figure 23.4
        
        Figure 23.5
        
        Figure 23.6
        
        Figure 23.7
        
        24.1 Introduction
        
        24.2 Validation Schemes
        
        24.3 Evaluation Measures
        
        24.3.1 Accuracy Related Measures
        
        24.3.1.1 Discrete Classifiers
        
        24.3.1.2 Probabilistic Classifiers
        
        24.4.1 Parametric Statistical Comparisons
        
        24.4.1.1 Pairwise Comparisons
        
        24.4.1.2 Multiple Comparisons
        
        24.4.2.1 Pairwise Comparisons
        
        24.4.2.2 Multiple Comparisons
        
        24.4.2.3 Permutation Tests
        
        Figure 24.1
        
        Figure 24.2
        
        Figure 24.3
        
        Figure 24.4
        
        Table 24.1
        
        Table 24.2
        
        Table 24.3
        
        Table 24.4
        
        Table 24.5
        
        25.1 Introduction
        
        25.2 Educational Resources
        
        25.2.1 Books on Data Classification
        
        25.2.2 Popular Survey Papers on Data Classification
        
        25.3.1 Data Benchmarks for Software and Research
        
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        Product information
        
        Title: Data Classification
        
        Author(s): Charu C. Aggarwal
        
        Release date: July 2014
        
        Publisher(s): Chapman and Hall/CRC
        
        ISBN: 9781498760584