Data Mining: Concepts, Models, Methods, and Algorithms [Paperback]

Mehmed Kantardzic (Author)

Book Description

Publication Date: October 25, 2002 | ISBN-10: 0471228524 | ISBN-13: 978-0471228523 | Edition: 1

A comprehensive introduction to the exploding field of data mining

We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis.

Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples.

This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided.

This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.

Editorial Reviews

Review

“…a very readable and up-to-date introduction to data mining…” (Quality & Reliability Engineering International, Vol. 21 (4) June 2005)

"…suitable for a graduate level course in data mining…I enjoyed reading this book and recommend it highly." (Journal of Statistical Computation & Simulation, April 2004)

“...clear and well understandable...recommended as basic guidance...practitioners will profit from the author's long experience..." (Zentralblatt Math, Vol. 1027, 2004)

“...reviews state-of-the-art techniques for analyzing enormous quantities of raw data...” (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003)

"…this is a comprehensive textbook that describes the process and methodologies of data mining in an unbiased manner…serves as an excellent starting point for anyone wishing to learn about data mining.” (Journal of Proteome Research, May/ June 2003)

"...a valuable book.... I truly enjoyed reading the book and I am glad to recommend it to anyone working in this fascinating field." (IIE Transactions)

"...detailed, well illustrated, and easy to understand...comprehensive…a good book..." (Mathematical Reviews 2003h)

"...this is probably the first data-mining book that I would select from my bookshelf as reading material for a statistician..." (Technometrics, Vol. 45, No. 3, August 2003)

From the Back Cover

A comprehensive introduction to the exploding field of data mining

We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis.

Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples.

This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided.

This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.

See all Editorial Reviews

Product Details

• Paperback: 360 pages
• Publisher: Wiley-IEEE Press; 1 edition (October 25, 2002)
• Language: English
• ISBN-10: 0471228524
• ISBN-13: 978-0471228523
• Product Dimensions: 7 x 0.8 x 10 inches
• Shipping Weight: 1.4 pounds
• Average Customer Review: 3.7 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #1,157,503 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

14 of 14 people found the following review helpful:

3.0 out of 5 stars Survey, not how-to, April 7, 2004

By 

wiredweird "wiredweird" (Earth, or somewhere nearby) - See all my reviews
(HALL OF FAME REVIEWER)    (TOP 500 REVIEWER)   

Amazon Verified Purchase

This review is from: Data Mining: Concepts, Models, Methods, and Algorithms (Paperback)

The subtitle advertises "concepts, models, methods, and algorithms". Concepts and models, yes; methods, a few; algorithms, nearly none that you could actually code.

This book's strength is its breadth. It offers brief tastes of many topics. It discusses early data preparation, including reduction of dimension and handling of outliers and missing values. It emphasizes that different kinds of questions must be addressed in different ways. The rest of the book then covers decision rules of different sorts, clustering, neural networks, genetic algorithms, fuzzy logic, and data visualization. Each chapter includes references and comments on what to expect from each reference - a nice touch. The end of the book names a wide variety of web sites, products, and companies dedicated to data mining.

The big problem, however, is that the book is shallow. With a few exceptions, it just names techniques instead of giving descriptions that a programmer can use. For example, the discussion of missing data barely mentions the idea that imputed (made-up) values must be tailored to the specific analysis technique, so as to minimize their effect on results. There are exceptions, of course. Neural nets get a relatively detailed treatment. The author gives illustrative examples of genetic algorithms, but those were thin and tangential to data mining. The section on data visualization could have been much more lively. There is a huge body of visual technique, some bordering on artistry, that can present high-dimensional data to the human pattern-detection faculty, and samples are readily available. This book's examples were small and drab, though. Also, it completely ignored the research in auditory and tactile data representation, and omitted discussion of graphic design principles required for effective presentation.

What really bothered me were examples of sheer carelessness. A number of figures, including 4.8 and 9.9, contain errors severe enough to interfere with the point being made. Important relationships are simply illegible. Books like this aren't cheap - I would have hoped that the author would show a little more respect for the people paying the money.

This book may have value as a survey resource, but isn't for the reader who wants to implement the algorithms. Its bibliography is informative, but not a major asset. Indices of current products and web sites nearly guarantee early obsolescence. Look this over thoroughly before you commit your time and money to it.

2 of 2 people found the following review helpful:

3.0 out of 5 stars Pattern recognition or machine learning, not data mining, August 22, 2004

By 

Y. Keselman (Chicago, IL United States) - See all my reviews
(REAL NAME)   

This review is from: Data Mining: Concepts, Models, Methods, and Algorithms (Paperback)

This book can be used as an introduction to pattern recognition or machine learning rather than into data mining. Data mining does appear here and there, but mostly it is the classical pattern recognition and machine learning material (data reduction, clustering, neural networks) with very few illustrations from data mining. An introduction into genetic algorithms and fuzzy sets is also in the book, just in case, I suppose. If you'd like more specific data mining knowledge, look elsewhere.

1 of 2 people found the following review helpful:

5.0 out of 5 stars Good introduction to Neural Networks, May 12, 2007

By 

Anhnhat Tran "Anhnhat Tran" (Reseda, CA USA) - See all my reviews
(REAL NAME)   

This review is from: Data Mining: Concepts, Models, Methods, and Algorithms (Paperback)

I am a graduate student doing thesis related to Neural Networks. I never took any class about Neural Networks before. After reading the chapter "Artificial Neural Networks", I found out that it is very readable for students who are new to Neural Networks. It shows clearly how to calculate/adjust weights with many examples, without overwhelming you with too much Math. I highly recommend this book if you are new to Neural Networks subject.