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 โ 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