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Data Mining: The Textbook, by Charu C. Aggarwal
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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
- Sales Rank: #283050 in Books
- Brand: Springer
- Published on: 2015-04-14
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.63" w x 7.00" l, .0 pounds
- Binding: Hardcover
- 734 pages
- Springer
Review
“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.”
Most helpful customer reviews
10 of 10 people found the following review helpful.
Complete coverage of data mining
By AKS
This book is a great resource on data mining. In the past, I found that these types of books are written either from a "data mining" perspective, or from a "machine learning" perspective. Data mining books frequently omit many basic machine learning methods such as linear, kernel, or logistic regression. However, machine learning books do not address basic data mining methods like association rules or outlier detection. This book finally provides about as complete coverage as one can hope to get from a single book. Two chapters are devoted to outlier detection. A remarkable and unusual feature is that methods for specific data types are covered in individually dedicated chapters. Even topic models are discussed, which are absent from information retrieval books.
8 of 8 people found the following review helpful.
Excellent breadth as well as depth
By Gabor
This is an excellent book both in depth and breadth of
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.
2 of 3 people found the following review helpful.
Data mining as a science
By Aran Joseph Canes
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 another so that one can see data mining as a discipline and not simply various techniques. One caveat, as somebody who makes a living by data mining, I can say that this text is more appropriate for academia than real analytic work. Practitioners with experience in data mining can understand the theoretical parts of data analysis better and undergraduate and graduate students can benefit from this theoretical perspective, but readers looking for an introduction to data mining in a real world setting should choose a different text.
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