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Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) [Hardcover]

Jiawei Han , Micheline Kamber
3.6 out of 5 stars  See all reviews (22 customer reviews)

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Book Description

July 6, 2011 0123814790 978-0123814791 3

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it’s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.

Since the previous edition’s publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today’s most powerful data mining techniques to meet real business challenges.



    * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data


    Frequently Bought Together

    Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) + Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) + Introduction to Data Mining
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    Editorial Reviews

    Amazon.com Review

    The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.

    Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges.

    • Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects.
    • Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.
    • Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

    Read a Sample Chapter from Data Mining: Concepts and Techniques
    Sample chapter from <i>Data Mining: Concepts and Techniques</i>
    Read a sample chapter from Data Mining: Concepts and Techniques

    Review

    ""[A] well-written textbook (2nd ed., 2006; 1st ed., 2001) on data mining or knowledge discovery. The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data-all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. Summing Up: Highly recommended. Upper-division undergraduates through 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 Reviews.com

    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 book is an extensive and detailed guide to the principal ideas, techniques and technologies of data mining. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book's coverage of underlying concepts. 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.""--BCS.org


    Product Details

    • Series: The Morgan Kaufmann Series in Data Management Systems
    • Hardcover: 744 pages
    • Publisher: Morgan Kaufmann; 3 edition (July 6, 2011)
    • Language: English
    • ISBN-10: 0123814790
    • ISBN-13: 978-0123814791
    • Product Dimensions: 1.7 x 7.6 x 9.4 inches
    • Shipping Weight: 3.2 pounds (View shipping rates and policies)
    • Average Customer Review: 3.6 out of 5 stars  See all reviews (22 customer reviews)
    • Amazon Best Sellers Rank: #41,274 in Books (See Top 100 in Books)

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    Customer Reviews

    Most Helpful Customer Reviews
    10 of 10 people found the following review helpful
    4.0 out of 5 stars Comprehensive Overview August 8, 2011
    Format:Hardcover|Vine Customer Review of Free Product (What's this?)
    Data Mining is a comprehensive overview of the field, and I think it is best for a graduate class in data mining, or perhaps as a reference book. The book's focus is on technique (i.e., how to analyze data, including preparation), and it addresses all the major topics in the field including data storage and pre-processing. However, the book is really about classification methods, and the 2 chapters on cluster analysis are particularly strong and thorough.

    For those looking for specific examples, applications, and domain knowledge, I would recommend Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management by Linoff & Berry. However, for analytic techniques, this reference book is far superior.
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    4 of 4 people found the following review helpful
    Format:Hardcover|Vine Customer Review of Free Product (What's this?)
    A text that makes it through a third edition means it is popular. This is intended for advanced undergraduate and first-year graduate level classes. Its structure is pure old-fashioned textbook. No bells, no whistles, no sidebars, no ornamentation. Necessary charts, illustrations and graphs are primitive.

    Fortunately, the two authors write in a reasonable clear way, pretty much free of academic phrasing.

    The goal is to teach the technology of turning masses of data into useful and usable information.

    The approach is very straight-forward and methodical. First, the authors explain what data mining is and move quickly into describing data, processing data, reducing data and, generally, organizing data for retrieval of information.

    There are exercises at the end of each chapter.

    The authors claim they wrote the book not only as a classroom text, but as "an excellent handbook" on the subject of data mining.

    It is that, but whether as a classroom student or on your own, you'd better have a reasonably solid understanding of statistics, match, C programming, database structure and more.

    In short, this is not an easy book for an easy subject.

    But it is a thorough, if very technical, introduction to data mining. Essentially only the serious need apply. Those who just need a general knowledge of data mining would best look elsewhere.

    Jerry
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    16 of 21 people found the following review helpful
    3.0 out of 5 stars Oriented for Academia October 16, 2011
    Format:Hardcover|Vine Customer Review of Free Product (What's this?)
    This was written to be a textbook from the start, complete with question-sets from at the end of every chapter. If you're a student you won't have any choice as to the book selection, however if you are looking at this more from a practical commercial standpoint you will have many choices and this may not be the best one. I think in many ways it tries to be very encyclopedic and covers a huge amount of background information that is probably perfunctory in industry. The book would be more useful as a desk reference with heavy editing, more real-life examples... perhaps along the lines of case studies that may fit outside of a curriculum based arc.

    Minuses:
    - Not very illustrative, when there are diagrams and visual examples they tend to be very bare bones
    - Some of the screen shots are absolutely terrible resolution (ex. page 602/603)
    Was this review helpful to you?
    6 of 7 people found the following review helpful
    Format:Hardcover|Vine Customer Review of Free Product (What's this?)
    This hard cover handbook and text in Machine Learning and Data Mining Techniques gives a wide and understandable overview of these methods. More than 80% of the text is readily understandable without recourse to advanced statistical and linear algebra methods, due to extensive verbal description of the nature of these algorithms and their applications, as well as illustrations and pseudocode algorithms. Unlike the other excellent text in the Morgan Kaufman series by Witten, Frank and Hall there is no emphasis on a particular data mining package (I own both texts). Slightly more treatment is provided of two important modern Machine Learning Methods--Neural Networks and Support Vector Machines.

    This is a modern and understandable treatment of the important topics of Data Mining and Machine Learning designed to be used as a classroom text.
    Comment | 
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    2 of 2 people found the following review helpful
    2.0 out of 5 stars Get the hardback version instead. February 16, 2013
    Format:Kindle Edition|Verified Purchase
    Viewing this in the Kindle reader was difficult. Many inset sections of text, including algorithms, appear as images in the text. These don't enlarge when I enlarge the font size and there seems to be no way to make them big enough to read. Even if they were bigger, the pixel size is large enough that they appear a little bit pixellated already. Enlarging them probably would exacerbate it.

    Get the hardback version instead.
    Comment | 
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    1 of 1 people found the following review helpful
    3.0 out of 5 stars KIndle version is OK but some scans are difficult to read February 24, 2013
    Format:Hardcover|Verified Purchase
    The content of the book seems pretty good. I have only got up to about chapter four so far and it's easy to read and introduces material in a reasonably gentle manner. The three stars are due to the fact that I am using the electronic version, on a kindle app on an Android tablet, and it has some issues. It is mostly OK but suffers because quite a lot of the technical material is scanned rather than true font or vector which means that if you zoom in, the surrounding text gets bigger but the figures, formula and diagrams often don't. This is particularly troublesome for mathematical formulas which are sometimes scanned at quiet low resolution and very difficult to decipher, not great when they are already quite complex to understand.

    It's usable though, much more convenient in electronic form and I've saved myself about $50 by renting the book on Amazon for four months compared to buying it at the Uni bookshop.
    Comment | 
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    Most Recent Customer Reviews
    4.0 out of 5 stars Data Mining Explained Perfectly
    I'm using this book for a Data Mining class in grad. school. I'd never taken a data mining course. Considering that it was a brand new topic to me, I'd say that I've come a long. Read more
    Published 1 month ago by Vincent
    5.0 out of 5 stars Comprehensive Textbook for Data Mining
    This is a robust and practical book explaining concept of data mining. I bought this as a textbook for Data Mining class at a grad school. Read more
    Published 5 months ago by Yasuaki Matsumoto
    3.0 out of 5 stars Decent Introduction to Data Mining
    This was a required book for my Data Mining & Business Intelligence class for the 2013 fall semester. Read more
    Published 7 months ago by Matticus Caesar
    5.0 out of 5 stars It's a great book.
    Good content.Nice condition. It's considered to be the best learning material for students and professionals who are new in this field.
    Published 10 months ago by Ouyang Weichen
    5.0 out of 5 stars Great book for concepts
    This is a great book for who is starting at data mining.

    It gives us a solid concept definition about data mining concepts, and common techniques. Read more
    Published 13 months ago by Clodoaldo Brasilino Leite Neto
    4.0 out of 5 stars Comprehensive and easy to digest
    As an experienced software engineer doing his first steps in the field of Data Mining, this book proved very useful in introducing the jargon, the basic concepts and the methods... Read more
    Published 15 months ago by Ido Tamir
    1.0 out of 5 stars this book is horrible
    It's just a bad textbook, without good real world examples, total theory without any application whatsoever. Read more
    Published 18 months ago by Amazon Customer
    1.0 out of 5 stars Too verbos
    I am skipping thru this book , and it is TOO LONG, TOO BASIC, AND PROGRESS TOO SLOW. I am reading few lines, skipping a page ... Read more
    Published 19 months ago by T. A. TSHUVA
    3.0 out of 5 stars Do not buy Kindle format
    The Kindle format is unusable. I had to buy a second copy from another seller who specializes in textbooks.

    The content is a good introduction. Read more
    Published on June 7, 2012 by Robert J. White
    4.0 out of 5 stars Very good book on Data Mining for Machine Learning practitioners
    I selected this book, hoping to understand the difference between Data Mining, which I wasn't familiar with yet, and the fields already known to me of Machine Learning and... Read more
    Published on June 2, 2012 by Daniel Korzekwa
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