Save Big On Open-Box & Pre-owned: Buy "Data Mining: The Textbook” from Amazon Warehouse Deals and save 7% off the $89.99 list price. Product is eligible for Amazon's 30-day returns policy and Prime or FREE Shipping. See all Open-Box & Pre-owned offers from Amazon Warehouse Deals.
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Data Mining: The Textbook 2015th Edition
Use the Amazon App to scan ISBNs and compare prices.
Frequently Bought Together
Customers Who Bought This Item Also Bought
Special Offers and Product Promotions
“Written by one of the most prodigious editors and authors in the data mining community, Data mining: the textbook is a comprehensive introduction to the fundamentals and applications of data mining. The recent drive in industry and academic toward data science and more specifically “big data” makes any well-written book on this topic a welcome addition to the bookshelves of experienced and aspiring data scientists… The writing style is excellent and the author managed to provide sufficient mathematical background in terms of formal proofs and notations, in order to make it self-contained and scientifically appealing to more theory-oriented readers.Covering more than 20 chapters and 700 pages, Aggarwal provides a unique textbook and reference to data mining, which I recommend to every reader working on or learning about data mining.” (Radu State, ACM Computing Reviews #CR143869)
About the Author
Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T.J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining. He has published more than 250 papers in refereed conferences and journals and authored over 80 patents. He is author or editor of 14 books, including the first comprehensive book on outlier analysis, which is written from a computer science point of view. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM.
He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, a recipient of the IBM Outstanding Technical Achievement Award (2009) for his work on data streams, and a recipient of an IBM Research Division Award (2008) for his contributions to System S. He also received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving data mining. He has served as the general co-chair of the IEEE Big Data Conference, 2014. He served as an associate editor of the IEEE Transactions on Knowledge and Data Engineering from 2004 to 2008. He is an associate editor of the ACM Transactions on Knowledge Discovery from Data, an action editor of the Data Mining and Knowledge Discovery Journal, editor-in- chief of the ACM SIGKDD Explorations, and an associate editor of the Knowledge and Information Systems Journal. He serves on the advisory board of the Lecture Notes on Social Networks, a publication by Springer. He has served as the vice-president of the SIAM Activity Group on Data Mining, which is responsible for all data mining activities organized by SIAM, including their main data mining conference. He is a fellow of the SIAM, the ACM, and the IEEE for “contributions to knowledge discovery and data mining algorithms.”
Top Customer Reviews
the topics covered. It gives descriptions, analyses, and insights
about the most popular algorithms on various topics, and it covers
many more areas than most books. The book is well integrated across
the broad diversity of topics that are covered, and connections between
methods and topics are pointed out throughout the book. I wouldn't
agree with an earlier review that the descriptions are short or
introductory. For most of the important topics, a lot of detail is
provided in terms of algorithm description and pseudo-code.
In some cases, interesting analyses are also provided. For instance,
in the case of frequent pattern mining algorithms,
not only are more algorithms discussed
than most of the other books, but a discussion of multiple choices
of data structures for the same algorithm is provided,
along with their relative trade-offs. The relationships among various
algorithms are also discussed. I have seen quite a
few textbooks on data mining, and I have not seen anything close to this
level of detail in any of the other books. Overall, my impression is
that the author has done an excellent job of calibrating detail level
to topic importance. Therefore, it can serve both as a textbook and
as a reference book. On the other hand, this is certainly
not an implementation or programming-centric book. The book is good at
teaching principles and concepts.
Most Recent Customer Reviews
Loved the textbook. Had to buy it for a college course, although, I found it cheaper on ebay!
This author is awesome, this book is awesome, this topic is awesome. This guy is a leader in the field.Published 18 months ago by Jennifer A Evans
As far as I know, this is the first book to try to systematize data mining into a science. Dr. Aggarwal does a good job of relating the different parts of data mining to one... Read morePublished 18 months ago by Aran Joseph Canes