Data Mining Principles

Author: Hand
Publisher:
Publish Date: 2003-04-01
Features: Many disciplines face a common problem: how to store, access, and model extremely large datasets for description and understanding? These issues have led to growing interest in data mining technologies. For a long time, many independent disciplines have separately focused on various aspects of data mining. This book integrates the application of data mining for information science, computer science, and statistics students, making it a truly interdisciplinary textbook. The book is composed of several parts.
Part I is the foundation, introducing the fundamental principles on which data mining algorithms and their applications rely. The discussions are intuitive, easy to understand, and presented in a clear and accessible manner.
Part II covers data mining algorithms, systematically discussing how to construct algorithms to solve specific problems. The content includes trees and rules for classification and regression, association rules, belief networks, traditional statistical models, and various nonlinear models such as neural networks and "memory-based" local models.
Part III introduces how to apply the algorithms and principles discussed earlier to solve real-world data mining problems. The topics include the role of metadata, handling missing data, and data preprocessing.
Against the backdrop of increasingly vast data resources, there is a pressing need for powerful tools to "mine" valuable information from them. Data mining is an emerging interdisciplinary field that combines the contents of statistics, machine learning, databases, and artificial intelligence to address this demand. This book delves into the principles of data mining, integrating the contributions of information science, computer science, and statistics, making it a truly interdisciplinary textbook. It is suitable for senior undergraduate and graduate students in computer science and applied mathematics, as well as researchers and professionals dedicated to the field of data mining.