Data Mining Tutorial

Author: Dumma
Publisher:
Publish Date: 2003-10-01
Features: This book comprehensively and systematically introduces the basic concepts, methods, and algorithms of data mining, making it an excellent choice for systematically learning data mining. The book is divided into four parts: Part I is an introduction, which provides a comprehensive overview of the background information, related concepts, and main technologies used in data mining; Part II focuses on the core algorithms of data mining, systematically and deeply describing common algorithms used for classification, clustering, and association rules; Part III covers advanced topics in data mining, primarily discussing Web mining, spatial data mining, temporal and sequential data mining; Part IV is an appendix, introducing some popular data mining tool products in the market, including product names, product functions, suppliers, technologies used, operating platforms, and product status. Data mining is a technology that has emerged in recent years alongside the establishment of large-scale database systems and the widespread use of the World Wide Web. It is an emerging technology that combines the fields of databases, machine learning, and statistics. This book comprehensively and systematically introduces the basic concepts, methods, and algorithms of data mining, making it an excellent choice for systematically learning data mining. The book is divided into four parts: Part I is an introduction, which provides a comprehensive overview of the background information, related concepts, and main technologies used in data mining; Part II focuses on the core algorithms of data mining, systematically and deeply describing common algorithms used for classification, clustering, and association rules; Part III covers advanced topics in data mining, primarily discussing Web mining, spatial data mining, temporal and sequential data mining; Part IV is an appendix, introducing some popular data mining tool products in the market, including product names, product functions, suppliers, technologies used, operating platforms, and product status. The book is well-structured, with clear key points, accurate expressions, and a complete system. For each algorithm, the book not only provides detailed explanations but also includes examples and pseudocode. The exercises and references at the end of each chapter provide readers with clues for further exploring related issues. This book is suitable as a textbook for graduate students and senior undergraduate students in computer science, as well as a reference book for researchers in related fields.