Introduction to Data Mining
2nd Edition
Pearson Education · Print & ebook · January 4, 2018
Introducing the fundamental concepts and algorithms of data mining Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals.
We earn from qualifying purchases.
- Who it’s for
- Useful for students, instructors, researchers, and data mining professionals
- Themes
- Data mining fundamentals, Classification and association analysis, Cluster analysis
- Genres
- Data Mining, Machine Learning, Data Warehousing
Nearest books
Matched by what the books are about, then ordered by what their readers also loved.
Concrete MathematicsRonald L. Graham, Donald E. Knuth52% match
Discovering Knowledge in DataDaniel T. Larose, Chantal D. Larose82% matchShares Data Mining and Machine Learning
Introducing Data ScienceDavy Cielen, Arno Meysman62% matchShares Machine Learning
Data Science for BusinessFoster Provost, Tom Fawcett61% matchShares Data Mining and Machine Learning
Introduction to Parallel ComputingAnanth Grama, Anshul Gupta61% match
Data Structures and Algorithms in JavaRobert Lafore59% match
Data Analysis Using SQL and ExcelGordon S. Linoff58% matchShares Data Mining
Introduction to AlgorithmsThomas H. Cormen, Charles E. Leiserson57% match
Start with three books, and this one can be the first.
Members get a new edition every morning: a route out from their books to essays, research and talks, with the reason for each.
Start with this bookAbout this book
Introducing the fundamental concepts and algorithms of data mining Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals.
Continue reading
Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.
Who it’s for
- Useful for students, instructors, researchers, and data mining professionals
- For readers seeking background to apply data mining to real problems
Where it sits
| Machine Learning | 85% |
|---|---|
| Data Mining | 40% |
| Data Warehousing | 19% |
Percentages are BookFrontier’s reading of the book.
Publisher categories
- Data Warehousing
- Authors
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar
- Publisher
- Pearson Education
- Published
- January 4, 2018
- Format
- Print & ebook
- Theme
- Data mining fundamentals · Classification and association analysis · Cluster analysis
- Reading lane
- Data Warehousing