Optimization Based Data Mining

Support Vector Machines for Classification Problems.- Method of Maximum Margin.-Dual Problem.-Soft Margin.- C- Support Vector Classification.-C- Support Vector Classification with Nominal Attributes.-LOO Bounds for Support Vector Machines.-Introduction .-LOO bounds for ε−Support Vector Regression.- LOO Bounds for Support Vector Ordinal Regression Machine .- Support Vector Machines for Multi-Class Classification Problems.-K-class Linear Programming Support Vector Classification Regression Machine (KLPSVCR).-Support Vector Ordinal Regression Machine for Multi-class Problems.- Unsupervised and Semi-Supervised Support Vector Machines.-Unsupervised and Semi-Supervised ν-Support Vector Machine.-Numerical Experiments.-Unsupervised and Semi-supervised Lagrange Support Vector Machine.-Unconstrained Transductive Support Vector Machine.-Robust Support Vector Machines.-Support Vector Ordinal Regression Machine.- Robust Multi-class Algorithm.- Robust Unsupervised and Semi-Supervised Bounded C-Support Vector Machine.-Feature Selection via lp-norm Support Vector Machines.-lp-norm Support Vector Classification.-lp-norm Proximal Support Vector Machine.-Multiple Criteria Linear Programming.-Comparison of Support Vector Machine and Multiple Criteria Programming.-Multiple Criteria Linear Programming.-Multiple Criteria Linear Programming for Multiple Classes.- Penalized Multiple Criteria Linear Programming.-Regularized Multiple Criteria Linear Programs for Classification.-MCLP Extensions.-Fuzzy MCLP.-FMCLP with Soft Constraints.-FMCLP by Tolerances.-Kernel Based MCLP.- Knowledge Based MCLP.- Rough set based MCLP.- Regression by MCLP.-Multiple Criteria Quadratic Programming.-A General Multiple Mathematical Programming.-Multi-Criteria Convex Quadratic Programming Model Kernel based MCQP.- Non-additiveMCLP.-Non-additive Measures and Integrals.-Non-additive Classification Models.-Non-additive MCP.- Reducing the Time Complexity.-Hierarchical Choquet Integral.-Choquet Integral with Respect to K-additive Measure.-MC2LP.-MC2LP Classification.-Minimal Error and Maximal Between-class Variance Model.-Firm Financial Analysis.-Finance and Banking.-General Classification Process.-Firm Bankruptcy Prediction.- Personal Credit Management.- Credit Card Accounts Classification.-Two-class Analysis.-FMCLP Analysis.-Three-class Analysis.-Four-class Analysis.-Empirical Study and Managerial Significance of Four-class Models.- Health Insurance Fraud Detection.- Problem Identification.-A Real-life Data Mining Study.- Network Intrusion Detection.-Problem and Two Datasets.-Classify NeWT Lab Data by MCMP, MCMP with Kernel and See5.-Classify KDDCUP-Data by Nine Different Methods.- Internet Service Analysis.-VIP Mail Dataset.- Empirical Study of Cross-validation.-Comparison of Multiple-Criteria Programming Models and SVM.-HIV-1 Informatics.-HIV-1 Mediated Neuronal Dendritic and Synaptic Damage.-Materials and Methods.-Designs of Classifications.- Analytic Results.- Anti-gen and Anti-body Informatics .-Problem Background.- MCQP,LDA and DT Analyses.-Kernel-based MCQP and SVM Analyses.-Geol-chemical Analyses.-Problem Description.- Multiple-class Analyses.-More Advanced Analyses.-Intelligent Knowledge Management.-Purposes of the Study.- Definitions and Theoretical Framework of Intelligent Knowledge.-Some Research Directions