TY - BOOK ID - 4863897 TI - Data Mining : The Textbook PY - 2015 SN - 9783319141428 3319141414 9783319141411 3319141422 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer Science. KW - Data Mining and Knowledge Discovery. KW - Pattern Recognition. KW - Computer science. KW - Data mining. KW - Optical pattern recognition. KW - Informatique KW - Exploration de données (Informatique) KW - Reconnaissance optique des formes (Informatique) KW - Engineering & Applied Sciences KW - Computer Science KW - Pattern recognition. KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Optical data processing KW - Pattern perception KW - Perceptrons KW - Visual discrimination KW - 54.64 KW - Pattern perception. KW - Computers KW - Computer Vision & Pattern Recognition. KW - Design perception KW - Pattern recognition KW - Form perception KW - Perception KW - Figure-ground perception UR - https://www.unicat.be/uniCat?func=search&query=sysid:4863897 AB - 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. ER -