Introduction to Data Mining PDF is a book by Michael Steinbach, Pang-Ning Tan, and Vipin Kumar is a must-have guide for anyone interested in the field of data mining. Published by Pearson Education in 2005, this book takes a comprehensive approach to understand data mining, with detailed explanations about different algorithms and techniques for analyzing large datasets.
The book covers a wide range of topics, from basic data mining concepts to advanced techniques such as neural networks and support vector machines. Since its initial publication, Introduction to Data Mining has become a bestseller, with over 200,000 copies sold worldwide. The book is available in both physical and digital formats, so readers can choose the format that suits them best.
The book has been praised for its clear and concise writing style, which makes it easy for readers to understand the material. It is also highly rated for its comprehensive coverage of data mining topics, with reviews citing the depth and breadth of the material as an impressive feature.
Table of Contents
Introduction to Data Mining Summary
Introduction to Data Mining by Michael Steinbach, Pang-Ning Tan, and Vipin Kumar is a comprehensive guide to data mining concepts and techniques. The authors introduce the fundamental concepts of data mining and describe how it can be used in a wide variety of application areas.
The book covers data pre-processing, clustering, and classification techniques, as well as topics such as text mining and web mining. It includes detailed discussions on a variety of data mining tools and algorithms, from basic to advanced.
The authors begin by introducing the fundamental concepts of data mining, including probability and statistical models, decision trees, artificial neural networks, and genetic algorithms. They then discuss the specific techniques used in data mining, including association analysis, clustering, classification, and regression.
Details of Introduction to Data Mining Book
|Book||Introduction to Data Mining|
|Author||Pang-Ning Tan, Michael Steinbach, Vipin Kumar|
|Originally published||January 1, 2005|
|Category||Textbook, Non Fiction|
|Publisher||Pearson Education India|
Multiple Languages Editions of Introduction to Data Mining Book
Introduction to Data Mining book has been translated into multiple languages, including Spanish, Chinese, and Turkish.
|Book Editions||Check Now|
About the Author
Michael Steinbach, Pang-Ning Tan, and Vipin Kumar are the authors of the Introduction to Data Mining book.
Michael Steinbach is an Associate Professor at the University of Minnesota’s Department of Computer Science and Engineering where he teaches several courses on data mining, machine learning, and bioinformatics.
Pang-Ning Tan has worked as a professor at Michigan State University since 1995 and also serves as the Director of Data Mining at the university’s Institute for Cyber-Enabled Research.
Vipin Kumar is a Regents Professor and Distinguished McKnight University Professor in the Department of Computer Science and Engineering, also at the University of Minnesota. He has been working on data mining since 1985 and currently serves as the director of the Institute for Data Science at the university.
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FAQs(Frequently Asked Questions)
What is the book Introduction to Data Mining about?
Introduction to Data Mining is an essential guide for anyone who wants to learn more about the principles and techniques of data mining.
How many pages is Introduction to Data Mining?
Introduction to Data Mining has a total of 546 pages.
What is the main idea of Introduction to Data Mining?
The main idea of Introduction to Data Mining is to help readers understand and utilize the tools, techniques, and methods for mining useful data from large datasets.
Is Introduction to Data Mining worth reading?
Introduction to Data Mining is an invaluable resource for anyone who wishes to learn more about the principles and techniques of data mining.
How long does it take to read the Introduction to Data Mining book?
It typically takes 8 to 10 hours to read the entire Introduction to Data Mining book.