Fuzzy Modeling and genetic algorithms for data mining and exploration (Paperback)

Description:

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you’ll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.

You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.

 

Table of Contents:

Preface
Acknowledgements
Introduction

PART ONE – CONCEPTS AND ISSUES

Chapter 1. FOUNDATIONS AND IDEAS
1.1 Enterprise Applications and Analysis Models
1.2 Distributed and Centralized Repositories
1.3 The Age of Distributed Knowledge
1.4 Information and Knowledge Discovery
1.5 Data Mining and Business Models
1.6 Fuzzy Systems for Business Process Models
1.7 Evolving Distributed Fuzzy Models
1.8 A Sample Case – Evolving a Model for Customer Segmentation
Review

Chapter 2. PRINCIPAL MODEL TYPES
2.1 Model and Event State Categorization
2.2 Model Type and Outcome Categorization
Review

Chapter 3. APPROACHES TO MODEL BUILDING
3.1 Ordinary Statistics.
3.2 Non-Parametric Statistics
3.3 Linear Regression In Statistical Models
3.4 Non-Linear Growth Curve Fitting
3.5 Cluster Analysis
3.6 Decision Trees and Classifiers
3.7 Neural Networks
3.8 Fuzzy SQL Systems
3.9 Rule Induction and Dynamic Fuzzy Models
Review
References

PART TWO – FUZZY SYSTEMS

Chapter 4. FUNDAMENTAL CONCEPTS OF FUZZY LOGIC
4.1 The Vocabulary of Fuzzy Logic
4.2 Boolean (Crisp) Sets – The Law of Bivalence
4.3 Fuzzy Sets
Review

Chapter 5. FUNDAMENTAL CONCEPTS OF FUZZY SYSTEMS
5.1 The Vocabulary of Fuzzy Systems
5.2 Fuzzy Rule-Based Systems – An Overview
5.3 Fuzzy Rules
5.4 Variable Decomposition Into Fuzzy Sets
5.5 A Fuzzy Knowledge Base – The Details
5.6 The Fuzzy Inference Engine
5.7 Inference Engine Approaches
5.8 Running A Fuzzy Model
Review

Chapter 6. FUZZYSQL AND INTELLIGENT QUERIES
6.1 The Vocabulary of Relational Databases and Queries
6.2 Basic Relational Database Concepts
6.3 Structured Query Language Fundamentals
6.4 Precision and Accuracy
6.5 Why do we search a database?
6.6 Expanding the Query Scope
6.7 Fuzzy Query Fundamentals
6.8 Measuring Query Compatibility
6.9 Complex Query Compatibility Metrics
6.10 Compatibility Threshold Management
6.11 FuzzySQL Process Flow
6.12 FuzzySQL Example
6.13 Evaluating the FuzzySQL Outcomes
Review
References

Chapter 7. FUZZY CLUSTERING
7.1 The Vocabulary of Fuzzy Clustering
7.2 Principles of Cluster Detection
7.3 Some General Clustering Concepts
7.4 Crisp Clustering Techniques
7.5 Fuzzy Clustering Concepts
7.6 Fuzzy c-Means Clustering
7.7 Fuzzy Adaptive Clustering
7.8 Generating Rule Prototypes
Review
References

Chapter 8. FUZZY RULE INDUCTION
8.1 The Vocabulary of Rule Induction
8.2 Rule Induction and Fuzzy Models
8.3 The Rule Induction Algorithm
8.4 The Model Building Methodology
8.5 A Rule Induction and Model Building Example
8.6 Measuring Model Robustness
Review
References
Technical Implementation
External Controls
Organization of the Knowledge Base
Executing A Fuzzy Rule

PART THREE – EVOLUTIONARY STRATEGIES

Chapter 9. FUNDAMENTAL CONCEPTS OF GENETIC ALGORITHMS
9.1 The Vocabulary of Genetic Algorithms
9.2 Overview
9.3 The Architecture of a Genetic Algorithm
Review
References

Chapter 10. GENETIC RESOURCE SCHEDULING OPTIMIZATION
10.1 The Vocabulary of Resource-Constrained Scheduling
10.2 Some Terminology Issues
10.3 Fundamentals
10.4 Objective Functions and Constraints
10.5 Bringing It All Together – Constraint Scheduling
10.6 A Genetic Crew Scheduler Architecture
10.7 Implementing and Executing the Crew Scheduler
10.8 Topology Constraint Algorithms and Techniques
10.9 Adaptive Parameter Optimization
Review
References

Chapter 11. GENETIC TUNING OF FUZZY MODELS
11.1 The Genetic Tuner Process
11.2 Configuration Parameters
11.3 Implementing and Running the Genetic Tuner
11.4 Advanced Genetic Tuning Issues
Review
References

描述：
《模糊建模和遺傳算法用於數據挖掘和探索》是一本針對商業和政府中開發數據挖掘模型的分析師、工程師和管理人員的手冊。正如您將會發現的那樣，模糊系統是表示和操作各種數據的非常有價值的工具，而從生物學中提取的遺傳算法和演化編程技術提供了設計和調整這些系統的最有效手段。

您不需要具備模糊建模或遺傳算法的背景知識，因為本書提供了詳細的方法指導，您可以立即應用於自己的項目中。作者提供了許多不同的例子，還有一個使用演化策略創建複雜排程系統的擴展示例。

目錄：
前言
致謝
引言

第一部分 - 概念和問題
第1章 - 基礎和思想
1.1 企業應用和分析模型
1.2 分佈式和集中式存儲庫
1.3 分佈式知識時代
1.4 信息和知識發現
1.5 數據挖掘和商業模型
1.6 用於業務流程模型的模糊系統
1.7 演化分佈式模糊模型
1.8 一個樣本案例 - 演化的客戶分割模型
回顧

第2章 - 主要模型類型
2.1 模型和事件狀態分類
2.2 模型類型和結果分類
回顧

第3章 - 模型構建方法
3.1 常規統計學
3.2 非參數統計學
3.3 線性回歸在統計模型中的應用
3.4 非線性增長曲線擬合
3.5 聚類分析
3.6 決策樹和分類器
3.7 神經網絡
3.8 模糊SQL系統
3.9 規則歸納和動態模糊模型
回顧
參考文獻

第二部分 - 模糊系統
第4章 - 模糊邏輯的基本概念
4.1 模糊邏輯的詞彙
4.2 布爾（清晰）集 - 二價律
4.3 模糊集
回顧

第5章 - 模糊系統的基本概念
5.1 模糊系統的詞彙
5.2 模糊規則系統 - 概述
5.3 模糊規則
5.4 變量分解為模糊集
5.5 模糊知識庫 - 詳細信息
5.6 模糊推理引擎
5.7 推理引擎方法
5.8 運行模糊模型
回顧

第6章 - 模糊SQL和智能查詢
6.1 關聯數據庫和查詢的詞彙
6.2 基本關聯數據庫概念
6.3 結構化查詢語言基礎
6.4 精度和準確度
6.5 為什麼要搜索數據庫？
6.6 擴大查詢範圍
6.7 模糊查詢基礎
6.8 測量查詢兼容性
6.9 複雜查詢兼容性度量
6.10 兼容性閾值管理
6.11 模糊SQL流程
6.12 模糊SQL示例
6.13 評估模糊SQL結果
回顧
參考文獻

第7章 - 模糊聚類
7.1 模糊聚類的詞彙
7.2 聚類檢測原則
7.3 一些常見的聚類