Introduction to Data Mining pdf is a popular textbook written by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. It provides an overview of the fundamental concepts and techniques of data mining and is widely used as a textbook in undergraduate and graduate-level data mining courses.
The book covers various aspects of data mining, such as data pre-processing, statistical methods, machine learning, and data visualization. It also includes real-world examples and case studies to illustrate the concepts. This book is available in different formats like audiobooks and paperback.
The book is designed to be accessible to a wide range of readers and assumes only a basic knowledge of statistics and computer science. It is suitable for students, researchers, and practitioners in the fields of computer science, statistics, and business.
Summary of Introduction to Data Mining
The book starts with an introduction to data mining and its applications, followed by a discussion of the data mining process, including data pre-processing, data visualization, and pattern evaluation. It then covers different data mining techniques including association rule mining, classification, clustering, and anomaly detection.
The book also covers advanced topics such as text mining, web mining, and social network analysis. In summary, the book provides a comprehensive introduction to the field of data mining, covering the key concepts, techniques, and algorithms used in the field, and providing a good balance between theory and practice.
The novel tells about real-world examples and case studies to illustrate the concepts. The book is designed to be accessible to a wide range of readers and assumes only a basic knowledge of statistics and computer science.
Introduction to Data Mining Book Details
| Book | Introduction to Data Mining |
| Author | Pang-Ning Tan, Michael Steinbach, and Vipin Kumar |
| Language | English |
| Published Date | 2009 |
| Publisher | Roy G. Perry College |
| Category | Computer Science, Business |
| Format | PDF, ePub |
| Number of Pages | 792 |
About the Author
Pang-Ning Tan is a professor at Michigan State University, where he teaches data mining and machine learning. He received his Ph.D. in computer science from the University of Minnesota. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the American Association for the Advancement of Science (AAAS).
Michael Steinbach is an Associate Professor at the University of Minnesota. He received his Ph.D. in Computer Science from the University of Minnesota. His research interests include data mining, machine learning, and high-performance computing. He has published more than 50 papers in these areas and has served as an associate editor for several journals.
Vipin Kumar is a professor at the University of Minnesota. He received his Ph.D. in Computer Science from the University of California, Berkeley. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of the Association for Computing Machinery (ACM).
All three authors have extensive research and teaching experience in the field of data mining and have published numerous papers in top-tier conferences and journals. They have also been involved in various data mining research projects and have a wealth of experience in the field.
Introduction to Data Mining Book Multiple Languages Editions
This classic book is available in different languages and you can achieve it in different countries all over the world.
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FAQs (Frequently Asked Questions)
What is the book Introduction to data mining?
“Introduction to Data Mining” is a popular textbook that provides an overview of the fundamental concepts and techniques of data mining.
What is the genre of Introduction to data mining?
The genre of “Introduction to Data Mining” is a textbook in the field of computer science, statistics, and business.
Is Introduction to data mining hard to read?
It depends on the reader’s background and familiarity with the topic, but the book is designed to be accessible to a wide range of readers and assumes only a basic knowledge of statistics and computer science.
What is the main theme of Introduction to data mining?
The main theme of this novel is to provide an overview of the fundamental concepts and techniques of data mining.
Is Introduction to data mining best for readers of all ages?
The book is primarily intended for students, researchers, and practitioners in the fields of computer science, statistics, and business.
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