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.
He served as an associate editor o.
He has served as the general co-chair of the IEEE Big Data Conference, 2014.
He also received the EDBT 2014 Test of Time Award for his work on condensation-based privacy-preserving Data mining.
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.
Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM.
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.
He has published more than 250 papers in refereed conferences and journals and authored over 80 patents.
He has worked extensively in the field of Data mining. from the Massachusetts Institute of Technology in 1996. from IIT Kanpur in 1993 and his Ph.
D.
He completed his B.
S.
Watson Research Center in Yorktown Heights, New York.
Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T.
J.
Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago About the Author Charu C.
It is a great book for graduate students and researchers as well as practitioners." -- Philip S.
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\'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.
The book is complete with theory and practical use cases.
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.
Praise for Data Mining: The Textbook - "As I read through this book, I have already decided to use it in my classes.
Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.
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).
Appropriate for both introductory and advanced Data mining courses, Data Mining: The Textbook balances mathematical details and intuition.
The domain chapters also have an applied flavor.
Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation.
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.
These chapters comprehensively discuss a wide variety of methods for these problems.
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.
Until now, no single book has addressed all these topics in a comprehensive and integrated way.
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.
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
