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

Format:

Book

Author/Creator:

Shi, Yong, 1956- author.

Tian, Yingjie, 1973- author.

Kou, Gang, author.

Peng, Yi, author.

Li, Jianping, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Advanced information and knowledge processing 1610-3947

Advanced Information and Knowledge Processing, 1610-3947

Language:

English

Subjects (All):

Data mining.

Computer input-output equipment.

Data Mining and Knowledge Discovery.

Input/Output and Data Communications.

Local Subjects:

Data Mining and Knowledge Discovery.

Input/Output and Data Communications.

Physical Description:

1 online resource (XVI, 316 pages).

Edition:

First edition 2011.

Contained In:

Springer eBooks

Place of Publication:

London : Springer London : Imprint: Springer, 2011.

System Details:

text file PDF

Summary:

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. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors' research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

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.

Other Format:

Printed edition:

ISBN:

978-0-85729-504-0

9780857295040

Access Restriction:

Restricted for use by site license.