Optimization Based Data Mining: Theory and Applications

Optimization Based Data Mining: Theory and Applications by Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations....

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Bibliographic Details
Main Authors: Shi, Yong (Author), Tian, Yingjie (Author), Kou, Gang (Author), Peng, Yi (Author), Li, Jianping (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: London : Springer London : Imprint: Springer, 2011.
Edition:1st ed. 2011.
Series:Advanced Information and Knowledge Processing,
Springer eBook Collection.
Subjects:
Data mining.
Input-output equipment (Computers).
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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-additiveMeasures 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.

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