Note: Title -- Preface -- Contents -- Data Mining Techniques in Clustering, Association and Classification -- Introduction -- Data -- Knowledge -- Clustering -- Association -- Classification -- Data Mining -- Methods and Algorithms -- Applications -- Chapters Included in the Book -- Conclusion -- References -- Clustering Analysis in Large Graphs with Rich Attributes -- Introduction -- General Issues in Graph Clustering -- Graph Partition Techniques -- Basic Preparation for Graph Clustering -- Graph Clustering with SA-Cluster -- Graph Clustering Based on Structural/Attribute Similarities -- The Incremental Algorithm -- Optimization Techniques -- The Storage Cost and Optimization -- Matrix Computation Optimization -- Parallelism -- Conclusion -- References -- Temporal Data Mining: Similarity-Profiled Association Pattern -- Introduction -- Similarity-Profiled Temporal Association Pattern -- Problem Statement -- Interest Measure -- Mining Algorithm -- Envelope of Support Time Sequence -- Lower Bounding Distance -- Monotonicity Property of Upper Lower-Bounding Distance -- SPAMINE Algorithm -- Experimental Evaluation -- Related Work -- Conclusion -- References -- Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification -- Introduction -- Bayesian Networks -- Credal Sets -- Definition -- Basic Operations with Credal Sets -- Credal Sets from Probability Intervals -- Learning Credal Sets from Data -- Credal Networks -- Credal Network Definition and Strong Extension -- Non-separately Specified Credal Networks -- Computing with Credal Networks -- Credal Networks Updating -- Algorithms for Credal Networks Updating -- Modelling and Updating with Missing Data -- An Application: Assessing Environmental Risk by Credal Networks -- Debris Flows -- The Credal Network -- Credal Classifiers -- Naive Bayes -- Mathematical Derivation. , Naive Credal Classifier (NCC) -- Comparing NBC and NCC in Texture Recognition -- Treatment of Missing Data -- Metrics for Credal Classifiers -- Tree-Augmented Naive Bayes (TAN) -- Variants of the Imprecise Dirichlet Model: Local and Global IDM -- Credal TAN -- Further Credal Classifiers -- Lazy NCC (LNCC) -- Credal Model Averaging (CMA) -- Open Source Software -- Conclusions -- References -- Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets -- Introduction: Hierarchy and Other Symmetries in Data Analysis -- About This Article -- A Brief Introduction to Hierarchical Clustering -- A Brief Introduction to p-Adic Numbers -- Brief Discussion of p-Adic and m-Adic Numbers -- Ultrametric Topology -- Ultrametric Space for Representing Hierarchy -- Some Geometrical Properties of Ultrametric Spaces -- Ultrametric Matrices and Their Properties -- Clustering through Matrix Row and Column Permutation -- Other Miscellaneous Symmetries -- Generalized Ultrametric -- Link with Formal Concept Analysis -- Applications of Generalized Ultrametrics -- Example of Application: Chemical Database Matching -- Hierarchy in a p-Adic Number System -- p-Adic Encoding of a Dendrogram -- p-Adic Distance on a Dendrogram -- Scale-Related Symmetry -- Tree Symmetries through the Wreath Product Group -- Wreath Product Group Corresponding to a Hierarchical Clustering -- Wreath Product Invariance -- Example of Wreath Product Invariance: Haar Wavelet Transform of a Dendrogram -- Remarkable Symmetries in Very High Dimensional Spaces -- Application to Very High Frequency Data Analysis: Segmenting a Financial Signal -- Conclusions -- References -- Randomized Algorithm of Finding the True Number of Clusters Based on Chebychev Polynomial Approximation -- Introduction -- Clustering -- Clustering Methods -- Stability Based Methods. , Geometrical Cluster Validation Criteria -- Randomized Algorithm -- Examples -- Conclusion -- References -- Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters -- Introduction -- Background -- Partitional Clustering Using Bregman Divergences -- Density-Based and Mode Seeking Approaches to Clustering -- Iterative Relocation Algorithms for Finding a Single Dense Region -- Clustering a Subset of Data into Multiple Overlapping Clusters -- Bregman Bubble Clustering -- Cost Function -- Problem Definition -- Bregmanian Balls and Bregman Bubbles -- BBC-S: Bregman Bubble Clustering with Fixed Clustering Size -- BBC-Q: Dual Formulation of Bregman Bubble Clustering with Fixed Cost -- Soft Bregman Bubble Clustering (Soft BBC) -- Bregman Soft Clustering -- Motivations for Developing Soft BBC -- Generative Model -- Soft BBC EM Algorithm -- Choosing an Appropriate p0 -- Improving Local Search: Pressurization -- Bregman Bubble Pressure -- Motivation -- BBC-Press -- Soft BBC-Press -- Pressurization vs. Deterministic Annealing -- A Unified Framework -- Unifying Soft Bregman Bubble and Bregman Bubble Clustering -- Other Unifications -- Example: Bregman Bubble Clustering with Gaussians -- 2 Is Fixed -- 2 Is Optimized -- ``Flavors" of BBC for Gaussians -- Mixture-6: An Alternative to BBC Using a Gaussian Background -- Extending BBOCC & -- BBC to Pearson Distance and Cosine Similarity -- Pearson Correlation and Pearson Distance -- Extension to Cosine Similarity -- Pearson Distance vs. (1-Cosine Similarity) vs. Other Bregman Divergences - Which One to Use Where? -- Seeding BBC and Determining k Using Density Gradient Enumeration (DGRADE) -- Background -- DGRADE Algorithm -- Selecting sone: The Smoothing Parameter for DGRADE -- Experiments -- Overview -- Datasets -- Evaluation Methodology -- Results for BBC with Pressurization. , Results on BBC with DGRADE -- Concluding Remarks -- References -- DepMiner: A Method and a System for the Extraction of Significant Dependencies -- Introduction -- Related Work -- Estimation of the Referential Probability -- Setting a Threshold for -- Embedding n in Algorithms -- Determination of the Itemsets Minimum Support Threshold -- System Description -- Experimental Evaluation -- Conclusions -- References -- Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries -- Introduction -- Frequent Itemset Mining and Apriori Algorithm -- Basic Definitions and Problem Statement -- Algorithm Apriori -- Frequent Itemset Queries - State of the Art -- Frequent Itemset Queries -- Constraint-Based Frequent Itemset Mining -- Reusing Results of Previous Frequent Itemset Queries -- Optimizing Sets of Frequent Itemset Queries -- Basic Definitions -- Problem Formulation -- Related Work on Multi-query Optimization -- Common Counting -- Basic Algorithm -- Motivation for Query Set Partitioning -- Key Issues Regarding Query Set Partitioning -- Frequent Itemset Query Set Partitioning by Hypergraph Partitioning -- Data Sharing Hypergraph -- Hypergraph Partitioning Problem Formulation -- Computation Complexity of the Problem -- Related Work on Hypergraph Partitioning -- Query Set Partitioning Algorithms -- CCRecursive -- CCFull -- CCCoarsening -- CCAgglomerative -- CCAgglomerativeNoise -- CCGreedy -- CCSemiGreedy -- Experimental Results -- Comparison of Basic Dedicated Algorithms -- Comparison of Greedy Approaches with the Best Dedicated Algorithms -- Review of Other Methods of Processing Sets of Frequent Itemset Queries -- Conclusions -- References -- Text Clustering with Named Entities: A Model, Experimentation and Realization -- Introduction -- An Entity-Keyword Multi-Vector Space Model -- Measures of Clustering Quality. , Hard Clustering Experiments -- Fuzzy Clustering Experiments -- Text Clustering in VN-KIM Search -- Conclusion -- References -- Regional Association Rule Mining and Scoping from Spatial Data -- Introduction -- Related Work -- Hot-Spot Discovery -- Spatial Association Rule Mining -- The Framework for Regional Association Rule Mining and Scoping -- Region Discovery -- Problem Formulation -- Measure of Interestingness -- Algorithms -- Region Discovery -- Generation of Regional Association Rules -- Arsenic Regional Association Rule Mining and Scoping in the Texas Water Supply -- Data Collection and Data Preprocessing -- Region Discovery for Arsenic Hot/Cold Spots -- Regional Association Rule Mining -- Region Discovery for Regional Association Rule Scoping -- Summary -- References -- Learning from Imbalanced Data: Evaluation Matters -- Motivation and Significance -- Prior Work and Limitations -- Experiments -- Datasets -- Empirical Analysis -- Discussion and Recommendations -- Comparisons of Classifiers -- Towards Parts-Per-Million -- Recommendations -- Summary -- References -- Author Index.

Note: Title Page -- Preface -- Contents -- Editors -- Advances in Intelligent Data Mining -- Introduction -- Medical Influences -- Health Influences -- Social Influences -- Information Discovery -- On-Line Communities -- Biological Influences -- Biological Networks -- Estimations in Gene Expression -- Chapters Included in the Book -- Conclusion -- References -- Temporal Pattern Mining for Medical Applications -- Introduction -- Types of Temporal Data in Medical Domain -- Definitions -- Temporal Pattern Mining Algorithms -- Temporal Pattern Mining from a Set of Sequences -- Temporal Pattern Mining from a Single Sequence -- Medical Applications -- Conclusions -- References -- BioKeySpotter: An Unsupervised Keyphrase Extraction Technique in the Biomedical Full-Text Collection -- Introduction -- Backgrounds and Related Work -- The Proposed Approach -- Evaluation -- Dataset -- Comparison Algorithms -- Experimental Results -- Conclusion -- References -- Mining Health Claims Data for Assessing Patient Risk -- What Is Health Risk? -- Traditional Models for Assessing Health Risk -- Risk Factor-Based Risk Models -- Data Sources -- Enrollment Data -- Claims and Coding Systems -- Interpretation of Claims Codes -- Clinical Identification Algorithms -- Sensitivity-Specificity Trade-Off -- Constructing an Identification Algorithm -- Sources of Algorithms -- Construction and Use of Grouper Models -- Drug Grouper Models -- Drug-Based Risk Adjustment Models -- Summary and Conclusions -- References -- Mining Biological Networks for Similar Patterns -- Introduction -- Metabolic Network Alignment with One-to-One Mappings -- Model -- Problem Formulation -- Pairwise Similarity of Entities -- Similarity of Topologies -- Combining Homology and Topology -- Extracting the Mapping of Entities -- Similarity Score of Networks -- Complexity Analysis. , Metabolic Network Alignment with One-to-Many Mappings -- Homological Similarity of Subnetworks -- Topological Similarity of Subnetworks -- Combining Homology and Topology -- Extracting Subnetwork Mappings -- Significance of Network Alignment -- Identification of Alternative Entities -- Identification of Alternative Subnetworks -- One-to-Many Mappings within and across Major Clades -- Summary -- Further Reading -- References -- Estimation of Distribution Algorithms in Gene Expression Data Analysis -- Introduction -- Estimation of Distribution of Algorithms -- Model Building in EDA -- Notation -- Models with Independent Variables -- Models with Pair Wise Dependencies -- Models with Multiple Dependencies -- Application of EDA in Gene Expression Data Analysis -- State-of-Art of the Application of EDAs in Gene Expression Data Analysis -- Conclusion -- References -- Gene Function Prediction and Functional Network: The Role of Gene Ontology -- Introduction -- Gene Function Prediction -- Functional Gene Network Generation -- Related Work and Limitations -- GO-Based Gene Similarity Measures -- Estimating Support for PPI Data with Applications to Function Prediction -- Mixture Model of PPI Data -- Data Sets -- Function Prediction -- Evaluating the Function Prediction -- Experimental Results -- Discussion -- A Functional Network of Yeast Genes Using Gene Ontology Information -- Data Sets -- Constructing a Functional Gene Network -- Using Semantic Similarity (SS) -- Evaluating the Functional Gene Network -- Experimental Results -- Discussion -- Conclusions -- References -- Mining Multiple Biological Data for Reconstructing Signal Transduction Networks -- Introduction -- Background -- Signal Transduction Network -- Protein-Protein Interaction -- Constructing Signal Transduction Networks Using Multiple Data -- Related Work -- Materials and Methods. , Clustering and Protein-Protein Interaction Networks -- Evaluation -- Some Results of Yeast STN Reconstruction -- Outlook -- Summary -- References -- Mining Epistatic Interactions from High-Dimensional Data Sets -- Introduction -- Background -- Epistasis -- Detecting Epistasis -- High-Dimensional Data Sets -- Barriers to Learning Epistasis -- MDR -- Bayesian Networks -- Discovering Epistasis Using Bayesian Networks -- A Bayesian Network Model for Epistatic Interactions -- The BNMBL Score -- Experiments -- Efficient Search -- Experiments -- Discussion, Limitations, and Future Research -- References -- Knowledge Discovery in Adversarial Settings -- Introduction -- Characteristics of Adversarial Modelling -- Technical Implications -- Conclusion -- References -- Analysis and Mining of Online Communities of Internet Forum Users -- Introduction -- What Is Web 2.0? -- New Forms of Participation - Push or Pull? -- Internet Forums as New Forms of Conversation -- Social-Driven Data -- What Are Social-Driven Data? -- Data from Internet Forums -- Internet Forums -- Crawling Internet Forums -- Statistical Analysis -- Index Analysis -- Network Analysis -- Related Work -- Conclusions -- References -- Data Mining for Information Literacy -- Introduction -- Background -- Information Literacy -- Critical Literacy -- Educational Data Mining -- Towards Critical Data Literacy: A Frame for Analysis and Design -- A Frame of Analysis: Technique and Object -- On the Chances of Achieving Critical Data Literacy: Principles of Successful Learning as Description Criteria -- Examples: Tools and Other Approaches Supporting Data Mining for Information Literacy -- Analysing Data: Do-It-Yourself Statistics Visualization -- Analysing Language: Viewpoints and Bias in Media Reporting. , Analysing Data Mining: Building, Comparing and Re-using Own and Others' Conceptualizations of a Domain -- Analysing Actions: Feedback and Awareness Tools -- Analysing Actions: Role Reversals in Data Collection and Analysis -- Summary and Conclusions -- References -- Rule Extraction from Neural Networks and Support Vector Machines for Credit Scoring -- Introduction -- Re-RX: Recursive Rule Extraction from Neural Networks -- Multilayer Perceptron -- Finding Optimal Network Structure by Pruning -- Recursive Rule Extraction -- Applying Re-RX for Credit Scoring -- ALBA: Rule Extraction from Support Vector Machines -- Support Vector Machine -- ALBA: Active Learning Based Approach to SVM Rule Extraction -- Applying ALBA for Credit Scoring -- Conclusion -- References -- Using Self-Organizing Map for Data Mining: A Synthesis with Accounting Applications -- Introduction -- Data Pre-processing -- Types of Variables -- Distance Metrics -- Rescaling Input Variables -- Self-Organizing Map -- Introduction to SOM -- Formation of SOM -- Performance Metrics and Cluster Validity -- Extensions of SOM -- Non-metric Spaces -- SOM for Temporal Sequence Processing -- SOM for Cluster Analysis -- SOM for Visualizing High-Dimensional Data -- Financial Applications of SOM -- Case Study: Clustering Accounting Databases -- Data Description -- Data Pre-processing -- Experiments -- Results Presentation and Discussion -- References -- Applying Data Mining Techniques to Assess Steel Plant Operation Conditions -- Introduction -- Brief Description of EAF -- Performance Evaluation Criteria -- Innovations in Electric Arc Furnaces -- Details of the Operation -- Understanding SCIPs and Stages of a Heat -- Problem Description -- Data Mining Process -- Data -- Data Preprocessing -- Attribute Pruning -- The Experiments -- Data Mining Techniques -- Results -- Discussion. , Concluding Remarks -- References -- Author Index.

Note: Intro -- Preface -- Contents -- About the Editors -- 1 Advances in Biomedical Informatics: An Introduction -- Abstract -- 1.1 Introduction -- 1.2 Chapters of the Book -- 1.3 Conclusion -- 2 Digital Health Research Methods and Tools: Suggestions and Selected Resources for Researchers -- Abstract -- 2.1 Introduction -- 2.1.1 Understanding What Digital Health Means -- 2.1.2 Defining Your Research as Digital Health Research -- 2.1.3 Drawing from Health Informatics for Digital Health Research -- 2.1.4 Respecting Participation in Digital Health Research by Patients, Clients and Citizens -- 2.2 Methodological Considerations -- 2.2.1 Framing Your Digital Health Research -- 2.2.2 Contributing to the Clinical Evidence Base Through Digital Health Research -- 2.2.3 Positioning Digital Health Research as Health Services and Systems Research -- 2.2.4 Recognising Computer Science and Information Systems Research in Digital Health Research -- 2.3 Research Techniques -- 2.3.1 Reviewing Scientific and Technical Literature Related to Digital Health Research -- 2.3.2 Developing Tools as Part of Digital Health Research -- 2.3.3 Working with Data Collected from Digital Devices and Online Services -- 2.3.4 Collecting Data for Research About Digital Health -- 2.3.5 Research Data Management and Storage Planning -- 2.3.6 Writing up and Reporting Digital Health Research -- 2.4 Technology-Specific Resources -- 2.4.1 Looking into Specific Settings of Digital Health Research -- 2.4.2 Health Apps -- 2.4.3 Health Social Media -- 2.4.4 Health Self-Tracking with Consumer Wearables -- 2.5 Conclusion -- References -- 3 Machine Learning for Structured Clinical Data -- Abstract -- 3.1 Introduction -- 3.2 Uses of Machine Learning for Structured Clinical Data -- 3.2.1 Patient/Disease Stratification -- 3.2.2 Electronic phenotypes for Genetic Associations. , 3.2.3 Clinical Recommendations -- 3.3 Challenges of using Machine Learning in the Structured EHR -- 3.3.1 Limited "gold-standards" -- 3.3.2 Missing Data -- 3.3.3 Privacy, Reproducibility and Data Sharing -- 3.3.4 Longitudinal Data -- 3.4 Future and evolving opportunities for Machine Learning in the Structured EHR -- 3.4.1 Quantitative Electronic Phenotyping -- 3.4.2 Deep Learning, Unsupervised and Semi-Supervised Learning Approaches -- 3.4.3 Interpretation and the "Right to explanation" -- References -- 4 Defining and Discovering Interactive Causes -- Abstract -- 4.1 Introduction -- 4.2 Background -- 4.2.1 Bayesian Networks -- 4.2.2 Information Gain -- 4.3 Discrete Causal Interactions -- 4.3.1 Statistical Interactions -- 4.3.2 Interaction Dividend -- 4.3.3 Interaction Strength: A Discrete Causal Interaction Defined -- 4.4 Algorithms for Learning Causal Interactions -- 4.5 Reporting the Noteworthiness of an Interaction -- 4.6 Experiments -- 4.6.1 MBS-Gain -- 4.6.2 Exhaustive-Gain -- 4.7 Discussion -- Acknowledgements -- References -- 5 Bayesian Network Modeling for Specific Health Checkups on Metabolic Syndrome -- Abstract -- 5.1 Introduction -- 5.2 Specific Health Checkups and the Stratification of Individuals -- 5.2.1 Study Subjects -- 5.2.2 Results of Applying the Hierarchical Procedure -- 5.2.3 Binary Representation of Physical Examination Data -- 5.2.4 Constructing Bayesian Network Containing Examination and Questionnaire Data -- 5.3 Evaluation -- 5.3.1 Setting Nodes for Risk Assessment -- 5.3.2 Risk Evaluation by Setting Lifestyle Questionnaire Nodes -- 5.3.3 Comparison by Setting Changes in Lifestyle Questionnaire Nodes on Exercise Aspect -- 5.3.4 Evaluation of Actual Case -- 5.4 Conclusion -- References -- 6 Unsupervised Detection and Analysis of Changes in Everyday Physical Activity Data -- Abstract -- 6.1 Introduction -- 6.2 Related Work. , 6.3 Methods -- 6.3.1 Change Detection Algorithms -- 6.3.1.1 RuLSIF -- 6.3.1.2 Texture-Based Dissimilarity -- 6.3.1.3 sw-PCAR -- 6.3.1.4 Virtual Classifier -- 6.3.2 Change Significance Testing -- 6.3.3 Change Analysis -- 6.4 Results -- 6.4.1 Hybrid-Synthetic Dataset -- 6.4.2 B-Fit Dataset -- 6.5 Discussion -- 6.5.1 Hybrid-Synthetic Dataset -- 6.5.2 B-Fit Dataset -- 6.6 Conclusions -- Acknowledgements -- References -- 7 Machine Learning Applied to Optometry Data -- Abstract -- 7.1 Introduction -- 7.2 Classification of the Lipid Layer Patterns -- 7.2.1 Image Dataset -- 7.2.2 Methods -- 7.2.2.1 Image Analysis -- 7.2.2.2 Class Binarization -- 7.2.2.3 Feature Selection -- 7.2.2.4 Classification and Performance Measures -- 7.2.2.5 Multiple-Criteria Decision-Making and Rank Correlation -- 7.2.3 Results -- 7.3 Hyperemia Grading in the Bulbar Conjunctiva -- 7.3.1 Image Dataset -- 7.3.2 Methods -- 7.3.2.1 Conjunctiva Segmentation -- 7.3.2.2 Feature Computation -- 7.3.2.3 Feature Selection -- 7.3.2.4 Hyperemia Evaluation -- 7.3.3 Results -- 7.4 Tear Film Break-up Characterization -- 7.4.1 Video Dataset -- 7.4.2 Methods -- 7.4.2.1 Tear Film Video Preprocessing -- 7.4.2.2 Image Features -- 7.4.3 Results -- Acknowledgements -- References -- 8 Intelligent Decision Support Systems in Automated Medical Diagnosis -- Abstract -- 8.1 Introduction -- 8.2 State-of-the-Art IDSSs for CAMD -- 8.2.1 Neural Networks-Based IDSSs -- 8.2.1.1 Traditional NNs for CAMD -- 8.2.1.2 Applications in CAMD -- 8.2.2 Support Vector Machines-Based IDSSs -- 8.2.2.1 SVMs for CAMD -- 8.2.2.2 Applications in CAMD -- 8.2.3 Evolutionary-Inspired IDSSs and Other Approaches -- 8.3 How Useful Are IDSSs in CAMD? -- 8.4 Privacy Issues -- 8.5 Conclusions -- References -- 9 On The Automation of Medical Knowledge and Medical Decision Support Systems -- Abstract -- 9.1 Introduction. , 9.2 Ledley and Lusted Approach to Medical Diagnosis -- 9.3 Some Thoughts on Medical Reasoning -- 9.4 Analyzing Medical Knowledge Acquisition and Ambiguity -- 9.5 Methodological Synthesis -- 9.6 Imprecision, Uncertainty and Temporal Evolution -- 9.7 Analysis of a Practical Clinical Case -- 9.8 Discussion -- 9.9 Conclusions -- Acknowledgements -- References -- 10 Vital Signs Telemonitoring by Using Smart Body Area Networks, Mobile Devices and Advanced Signal Processing -- Abstract -- 10.1 Introduction -- 10.2 Physiologic Monitored Parameters -- 10.3 A Concrete Implementation: TELEMON Project -- 10.4 ECG Signal Processing for Arrhythmia Detection -- 10.5 Server Side -- 10.6 Client Side -- 10.7 Conclusions -- References -- Resource List -- II. Web Resources -- 11 Preprocessing in High Dimensional Datasets -- Abstract -- 11.1 Introduction -- 11.2 Discretization -- 11.2.1 Methods -- 11.2.2 Big Data Approaches -- 11.3 Feature Selection -- 11.3.1 GPU-Based Approaches -- 11.3.2 Distributed Feature Selection -- 11.3.2.1 Ensemble Feature Selection -- 11.3.3 Parallelization -- 11.4 New and Future Research Directions -- References -- 12 Gerotranscendental Change in the Elderly: An Analysis of 'Faith' and 'Leaving from Clinging to Self' -- Abstract -- 12.1 Introduction -- 12.2 Data -- 12.3 Analysis -- 12.4 Results -- 12.4.1 Superficial Obsessiveness -- 12.4.2 Changes in Interest in Things and Money -- 12.4.3 Changes in Happiness in Small Matters -- 12.4.4 Changes in Feelings of Being Made to Live -- 12.4.5 Changes in Faith -- 12.4.6 Changes in the Way of Feeling About a Supernatural Being, Such as God -- 12.4.7 Changes in a Sense of Fear Regarding Death -- 12.4.8 Changes in the Desire to Live Out One's Own Life -- 12.4.9 Conclusions Regarding Question D -- 12.5 Conclusion -- References.

Note: Intro -- Title -- Preface -- Contents -- Advanced Modelling Paradigms in Data Mining -- Introduction -- Foundations -- Statistical Modelling -- Predictions Analysis -- Data Analysis -- Chains of Relationships -- Intelligent Paradigms -- Bayesian Analysis -- Support Vector Machines -- Learning -- Chapters Included in the Book -- Conclusion -- References -- Data Mining with Multilayer Perceptrons and Support Vector Machines -- Introduction -- Supervised Learning -- Classical Regression -- Multilayer Perceptron -- Support Vector Machines -- Data Mining -- Business Understanding -- Data Understanding -- Data Preparation -- Modeling -- Evaluation -- Deployment -- Experiments -- Classification Example -- Regression Example -- Conclusions and Further Reading -- References -- Regulatory Networks under Ellipsoidal Uncertainty - Data Analysis and Prediction by Optimization Theory and Dynamical Systems -- Introduction -- Ellipsoidal Calculus -- Ellipsoidal Descriptions -- Affine Transformations -- Sums of Two Ellipsoids -- Sums of bold0mu mumu KKKKKK Ellipsoids -- Intersection of Ellipsoids -- Target-Environment Regulatory Systems under Ellipsoidal Uncertainty -- The Time-Discrete Model -- Algorithm -- The Regression Problem -- The Trace Criterion -- The Trace of the Square Criterion -- The Determinant Criterion -- The Diameter Criterion -- Optimization Methods -- Mixed Integer Regression Problem -- Conclusion -- References -- A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid -- Introduction -- The Knowledge Grid -- Workflow Components -- The DIS3GNO System -- Execution Management -- Use Cases and Performance -- Parameter Sweeping Workflow -- Ensemble Learning Workflow -- Related Work -- Conclusions -- References -- Formal Framework for the Study of Algorithmic Properties of Objective Interestingness Measures. , Introduction -- Scientific Landscape -- Database -- Association Rules -- Interestingness Measures -- A Framework for the Study of Measures -- Adapted Functions of Measure -- Expression of a Set of Measures -- Application to Pruning Strategies -- All-Monotony -- Universal Existential Upward Closure -- Optimal Rule Discovery -- Properties Verified by the Measures -- References -- Nonnegative Matrix Factorization: Models, Algorithms and Applications -- Introduction -- Standard NMF and Variations -- Standard NMF -- Semi-NMF (semiconvex) -- Convex-NMF (semiconvex) -- Tri-NMF (triNMF) -- Kernel NMF (LD2006) -- Local Nonnegative Matrix Factorization, LNMF (sparse1,sparse3) -- Nonnegative Sparse Coding, NNSC (coding) -- Spares Nonnegative Matrix Factorization, SNMF (SNMF1,SNMF2,CNMF) -- Nonnegative Matrix Factorization with Sparseness Constraints, NMFSC (NMFSC) -- Nonsmooth Nonnegative Matrix Factorization, nsNMF (nsnmf) -- Sparse NMFs: SNMF/R, SNMF/L (SNMF) -- CUR Decomposition (CUR) -- Binary Matrix Factorization, BMF (BMF,BMF2) -- Divergence Functions and Algorithms for NMF -- Divergence Functions -- Algorithms for NMF -- Applications of NMF -- Image Processing -- Clustering -- Semi-supervised Clustering -- Bi-clustering (co-clustering) -- Financial Data Mining -- Relations with Other Relevant Models -- Relations between NMF and K-means -- Relations between NMF and PLSI -- Conclusions and Future Works -- References -- Visual Data Mining and Discovery with Binarized Vectors -- Introduction -- Method for Visualizing Data -- Visualization for Breast Cancer Diagnistics -- General Concept of Using MDF in Data Mining -- Scaling Algorithms -- Algorithm with Data-Based Chains -- Algorithm with Pixel Chains -- Binarization and Monotonization -- Monotonization -- Conclusion -- References. , A New Approach and Its Applications for Time Series Analysis and Prediction Based on Moving Average of nth-Order Difference -- Introduction -- Definitions Relevant to Time Series Prediction -- The Algorithm of Moving Average of nth-order Difference for Bounded Time Series Prediction -- Finding Suitable Index m and Order Level n for Increasing the Prediction Precision -- Prediction Results for Sunspot Number Time Series -- Prediction Results for Earthquake Time Series -- Prediction Results for Pseudo-Periodical Synthetic Time Series -- Prediction Results Comparison -- Conclusions -- Appendix -- References -- Exceptional Model Mining -- Introduction -- Exceptional Model Mining -- Model Classes -- Correlation Models -- Regression Model -- Classification Models -- Experiments -- Analysis of Housing Data -- Analysis of Gene Expression Data -- Conclusions and Future Research -- References -- Online ChiMerge Algorithm -- Introduction -- Numeric Attributes, Decision Trees, and Data Streams -- VFDT and Numeric Attributes -- Further Approaches -- ChiMerge Algorithm -- Online Version of ChiMerge -- Time Complexity of Online ChiMerge -- Alternative Approaches -- A Comparative Evaluation -- Conclusion -- References -- Mining Chains of Relations -- Introduction -- Related Work -- The General Framework -- Motivation -- Problem Definition -- Examples of Properties -- Extensions of the Model -- Algorithmic Tools -- A Characterization of Monotonicity -- Integer Programming Formulations -- Case Studies -- Experiments -- Datasets -- Problems -- Conclusions -- References -- Author Index.