Environment Data Mining Concepts And Techniques Book


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Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. .. The book gives quick introductions to database and data mining concepts. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) [Jiawei Han, Micheline Kamber, Jian Pei] on. Compre o livro Data Mining: Concepts and Techniques na confira as ofertas para livros em inglês e importados.

Data Mining Concepts And Techniques Book

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Purchase Data Mining: Concepts and Techniques - 3rd Edition. Print Book & E- Book. ISBN , Data Mining: Concepts and Techniques, Errata on the first and second printings of the book Data Warehouse and OLAP Technology for Data Mining. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which.

Summing Up: Highly recommended.

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. 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.

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

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. It adds cited material from about , 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.

Table of Contents

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.

Students should have some background in statistics, database systems, and machine learning and some experience programming. 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.

Top Authors

A broad range of topics are covered, from an initial overview of the field of data mining and its fundamental concepts, to data preparation, data warehousing, OLAP, pattern discovery and data classification. The final chapter describes the current state of data mining research and active research areas.

Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science specializing in artificial intelligence from Concordia University, Canada.

He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. We are always looking for ways to improve customer experience on Elsevier. We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.

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Thanks in advance for your time. Skip to content. About Elsevier. Search for books, journals or webpages All Pages Books Journals. Concepts and Techniques. View on ScienceDirect. Hardcover ISBN: Morgan Kaufmann. Published Date: Page Count: View all volumes in this series: Sorry, this product is currently out of stock.

Data Mining: Concepts and Techniques

Flexible - Read on multiple operating systems and devices. Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle. When you read an eBook on VitalSource Bookshelf, enjoy such features as: Access online or offline, on mobile or desktop devices Bookmarks, highlights and notes sync across all your devices Smart study tools such as note sharing and subscription, review mode, and Microsoft OneNote integration Search and navigate content across your entire Bookshelf library Interactive notebook and read-aloud functionality Look up additional information online by highlighting a word or phrase.

Institutional Subscription. The discussion of descriptive techniques is Regression called prediction by the completed with a brief study of statistical authors appears as an extension of the measures i. The former dispersion measures and their insightful deals with continuous values while the latter graphical display.

Association rules are midway Linear regression is clearly explained; between descriptive and predictive data multiple, nonlinear, generalized linear, and mining maybe closer to descriptive log-linear regression models are only techniques. They find interesting referenced in the text. Some ratio-scaled. A taxonomy of clustering buzzwordism about the role of data mining methods is proposed including examples for and its social impact can be found in this each category: partitioning methods e.

This categorization of clustering Why to Read This Book. The youth of this field are as appealing as the previous ones. Unfortunately, This book constitutes a superb these interesting techniques are only briefly example of how to write a technical textbook described in this book.

It is Space constraints also limit the written in a direct style with questions and discussion of data mining in complex types of answers scattered throughout the text that data, such as object-oriented databases, keep the reader involved and explain the spatial, multimedia, and text databases. Web reasons behind every decision.

The chapters are mostly self- contained, so they can be separately used to Practical Issues. In fact, describes some interesting examples of the you may even use the book artwork which is use of data mining in the real world i.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.

It is also the obvious choice for academic and professional classrooms. Data modeling techniques for data mining IBM. We believe number of attributes, the more efficient the that this book section would deserve a more mining process.

Techniques and Applications. Intelligent Data Mining: Cluster Analysis: Basic Concepts and Methods Flexible - Read on multiple operating systems and devices. Advanced Data Mining Techniques.

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