Data Mining
The Textbook
Does not imply availability
Description
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
AI Overview
The book "Data Mining: The Textbook" by Charu C. Aggarwal is a comprehensive guide to the field of data mining, covering a wide range of topics from the fundamentals to advanced data types and their applications. Here is a detailed overview:
Key Themes
Fundamental Chapters:
- The book begins with fundamental chapters that cover the four main problems of data mining: clustering, classification, association pattern mining, and outlier analysis. These chapters discuss a wide variety of methods for these problems, making the book suitable for both introductory and advanced data mining courses[1][2].
Domain Chapters:
- Domain chapters focus on specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. These chapters provide detailed discussions on the methods and their applications in various domains[1][2].
Application Chapters:
- Application chapters explore important applications of data mining such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. These chapters have an applied flavor, making the book relevant to both theoretical and practical aspects of data mining[1][2].
Plot Summary
The book is structured into three main categories: fundamental chapters, domain chapters, and application chapters. Each category is designed to provide a balanced approach to data mining, covering both mathematical details and intuitive explanations.
Fundamental Chapters: These chapters introduce the core concepts of data mining, including clustering, classification, association pattern mining, and outlier analysis. They provide a comprehensive overview of the methods used in these areas, making them accessible to both students and practitioners[1][2].
Domain Chapters: These chapters delve into specific domains of data, such as text, time-series, sequence, graph, and spatial data. They discuss the specialized methods and techniques used in each domain, highlighting their applications and challenges[1][2].
Application Chapters: These chapters focus on practical applications of data mining, including stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. They emphasize the real-world implications of data mining techniques and their impact on various industries and domains[1][2].
Critical Reception
The book has received positive reviews for its comprehensive coverage of data mining topics. Here are some key points:
Accessibility: The book is praised for its ability to balance mathematical details with intuitive explanations, making it accessible to students and practitioners with varying levels of mathematical background[1][2].
Comprehensive Coverage: It is noted that the book provides a unique integration of various data mining topics, including advanced data types like text, time-series, discrete sequences, spatial data, graph data, and social networks, which is not typically found in other textbooks[1][2].
Applications: The book’s focus on practical applications has been highlighted as a significant strength, as it prepares readers for real-world scenarios in data science and analytics[1][2].
Overall, "Data Mining: The Textbook" by Charu C. Aggarwal is a seminal work in the field of data mining, offering a broad and deep understanding of the subject that is essential for both beginners and advanced practitioners.
Availability
The book is available in both hardcover and electronic formats. The electronic version can be downloaded for free from Springerlink if the reader’s institution subscribes to the relevant e-book package. Additionally, a low-cost paperback edition is available for purchase[1].