Data Mining Multi-Attribute Decision System. Facilitating Decision Support Through Data Mining Technique by Hierarchical Multi-Attribute Decision Models

Inhaltsverzeichnis (Table of Contents)

• Chapter 1. Introduction to Data Mining and Decision Support
• 1.1 Introduction
• 1.2 The KDD Process
• 1.2.1 Developing and understanding of the application domain
• 1.2.2 Selecting and creating a data set
• 1.2.3 Pre-processing and cleansing
• 1.2.4 Data transformation
• 1.2.5 Choosing the appropriate Data Mining task
• 1.2.6 Choosing the Data Mining algorithm
• 1.2.7 Employing the Data Mining algorithm
• 1.2.8 Evaluation
• 1.2.9 Using the discovered knowledge
• 1.3 The Data Mining, a Step of the KDD Process
• 1.3.1 Database, Data Warehouse, or Other Information Repositories
• 1.3.2 Database or Data Warehouse Server
• 1.3.3 Knowledge Base
• 1.3.4 Data Mining Engine
• 1.3.5 Pattern Evaluation Module
• 1.3.6 Graphical User Interface
• 1.4. Data Mining Functionalities
• 1.4.1 Concept / Class Description: Characterization and Discrimination
• 1.4.2 Association Rule Mining
• 1.4.3 Classification and Prediction

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This book aims to provide a comprehensive guide to the application of data mining techniques for facilitating decision support, focusing on the use of hierarchical multi-attribute decision models. The book explores the various aspects of data mining and decision support systems, including the KDD process, data mining functionalities, and the integration of multi-attribute decision models.

• Data Mining Techniques
• Decision Support Systems
• Hierarchical Multi-Attribute Decision Models
• KDD Process
• Data Mining Functionalities

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 provides an introduction to data mining and decision support systems, exploring the KDD process, data mining functionalities, and the role of multi-attribute decision models. The chapter covers the different stages involved in the KDD process, including data selection, data transformation, algorithm selection, and evaluation. It also discusses various data mining functionalities, such as classification, prediction, and association rule mining. The chapter concludes by highlighting the importance of data mining in today's data-driven world and its role in enhancing decision-making.

Schlüsselwörter (Keywords)

Data mining, decision support, multi-attribute decision models, hierarchical models, KDD process, data transformation, algorithm selection, classification, prediction, association rule mining.