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

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

Jiawei Han , Micheline Kamber
1.0 out of 5 stars  See all reviews (1 customer review)

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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 Description

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


    Product details

    • Format: Kindle Edition
    • File Size: 8233 KB
    • Print Length: 626 pages
    • Page Numbers Source ISBN: 0123814790
    • Publisher: Morgan Kaufmann; 3 edition (9 Jun 2011)
    • Sold by: Amazon Media EU S.à r.l.
    • Language: English
    • ASIN: B0058NBJ2M
    • Text-to-Speech: Enabled
    • X-Ray:
    • Average Customer Review: 1.0 out of 5 stars  See all reviews (1 customer review)
    • Amazon Bestsellers Rank: #459,084 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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    Most Helpful Customer Reviews
    4 of 8 people found the following review helpful
    1.0 out of 5 stars Economist 8 Nov 2012
    Format:Hardcover
    I am reading this book , and it is TOO LONG, TOO BASIC, AND PROGRESS TOO SLOW. I am reading few lines, skipping a page ...I understand that some readers need slower pace but then it make sense to split it into two books. It does not make any sense (in a book about data mining) to explain what is a mean and the formula to compute it, and to spend two pages on it..

    Maybe a good choice for someone who wants to spend the time to get a good understanding of the subject but who is not familiar with statistics, math etc. He should be a fast reader though...
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    Amazon.com: 3.6 out of 5 stars  22 reviews
    10 of 10 people found the following review helpful
    4.0 out of 5 stars Comprehensive Overview 8 Aug 2011
    By Susan Katz - Published on Amazon.com
    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.
    17 of 22 people found the following review helpful
    3.0 out of 5 stars Oriented for Academia 17 Oct 2011
    By GX - Published on Amazon.com
    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)
    4 of 4 people found the following review helpful
    5.0 out of 5 stars Traditional text format is tedious, but content is excellent 21 Sep 2012
    By Jerry Saperstein - Published on Amazon.com
    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
    6 of 7 people found the following review helpful
    5.0 out of 5 stars Excellent Verbal, Mathematical & Programmatic Description of Data Mining and Machine Learning Techniques 22 Dec 2011
    By Ira Laefsky - Published on Amazon.com
    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.
    3 of 3 people found the following review helpful
    2.0 out of 5 stars Get the hardback version instead. 17 Feb 2013
    By Tom in Florida - Published on Amazon.com
    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.
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    Popular Highlights

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    Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. &quote;
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    Data discrimination is a comparison of the general features of the target class data objects against the general features of objects from one or multiple contrasting classes. &quote;
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    Classification is the process of finding a model (or function) that describes and distinguishes data classes or concepts. &quote;
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