Data Mining: Concepts And Techniques

Data Analytics & prediction with Machine Learning AlgorithmsA well-written textbook (2nd ed., 2006; 1st ed., 2001) on information mining or data discovery. The textual content is supported by a robust define. The authors protect a lot of the introductory materials, however add the newest methods and developments in information mining, thus making this a complete useful resource for each newbies and practitioners. The focus is information—all points. The presentation is broad, encyclopedic, and complete, with ample references for readers to pursue in-depth analysis on any approach. Summing Up: Highly beneficial. Upper-division undergraduates by professionals/practitioners.” –CHOICE

“This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining–the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers.” –ACM’s Computing

“We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses.” –Gregory Piatetsky, President, KDnuggets

“Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines)…. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book.” –From the foreword by Christos Faloutsos, Carnegie Mellon University

“A very good textbook on data mining, this third edition reflects the changes that are occurring in the data mining field. It adds cited material from about 2006, a new section on visualization, and pattern mining with the more recent cluster methods. It’s a well-written text, with all of the supporting materials an instructor is likely to want, including Web material support, extensive problem sets, and solution manuals. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. That being said, readers are expected to have some coding experience, as well as database design and statistics analysis knowledge…Two additional items are worthy of note: the text’s bibliography is an excellent reference list for mining research; and the index is very complete, which makes it easy to locate information. Also, researchers and analysts from other disciplines–for example, epidemiologists, financial analysts, and psychometric researchers–may find the material very useful.” –Computing Reviews

“Han (engineering, U. of Illinois-Urbana-Champaign), Micheline Kamber, and Jian Pei (both computer science, Simon Fraser U., British Columbia) present a textbook for an advanced undergraduate or beginning graduate course introducing data mining. Students should have some background in statistics, database systems, and machine learning and some experience programming. Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers. Chapter-end exercises are included.” –SciTech Book News

“This guide is an in depth and detailed information to the principal concepts, methods and applied sciences of knowledge mining. The guide is organised in 13 substantial chapters, every of which is actually standalone, however with helpful references to the guide’s protection of underlying ideas. A broad vary of matters are coated, from an preliminary overview of the sphere of knowledge mining and its basic ideas, to information preparation, information warehousing, OLAP, sample discovery and information classification.

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