- Paperback: 408 pages
- Publisher: Addison Wesley; 1 edition (26 Sep 2002)
- Language English
- ISBN-10: 0201741288
- ISBN-13: 978-0201741285
- Product Dimensions: 23.2 x 18.9 x 1.7 cm
- Amazon Bestsellers Rank: 1,420,298 in Books (See Top 100 in Books)
- See Complete Table of Contents
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More About the Author
Product Description
Product Description
This primer on data mining provides an introduction to the principles and techniques for extracting information from a business-minded perspective. A basic familiarity with the field of data mining concepts is built and then enhanced via 13 data mining tutorials. Upon completion of these tutorials, students will be fully able to data mine. This book is appropriate for students of CS, MIS, and Information Technology.
About the Author
Richard J. Roiger is a professor of computer science at Minnesota State University, Mankato and a senior software engineer for Information Acumen Corporation (www.infoacumen.com). Richard received a Ph.D. degree in Computer Science from the University of Minnesota in 1991. His research interests include machine learning, knowledge discovery in databases and expert systems. He is a member of the American Association of Artificial Intelligence, the Association for Computing Machinery and IEEE. He is also a musician and songwriter, enjoys spending time with his children and grandchildren and likes playing golf as time permits.
Michael W. Geatz is currently President of Biosensor Research Institute of America Inc. (dba Giant Medical). Formerly, he was Vice President of PulseTracer Technologies Inc., a division of $1.5 billion Zynik Capital Corp. and a software consultant to the financial and medical device industries. He was also co-founder of an artificial intelligence company, Information Acumen Corp., is a named inventor of a patented piezoelectric switch for use in radio frequency identification (RFID) chips as well as the world's first wrist pulse sensor, and is proud of his civilian service to the U.S. Army during the first Gulf war. Educational credentials include an MBA from Golden Gate University in 1991 and a Computer Science degree from University of North Dakota in 1984.
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8 reviews
13 of 13 people found the following review helpful
Overall, a very good hands-on book for learning data mining
27 Jan 2004
Format:Paperback|Amazon Verified Purchase
The book does a good job covering basic data mining terminology and concepts, and provides introductory, but still subtantial coverage of the most common data mining methods.
Positives: a trial version of the easy-to-use Excel-based iDA tool is included with the book, which allows the reader to reproduce the examples (very helpful for understanding the text). iDA may also be used to complete many of the well thought out exercises provided at the end of each chapter. Working with the hands-on examples and exercises is an excellent way to learn data mining, and due to this, the book provides unique and excellent value.
Negatives: the order of topics and chapters seems rather disorganized; topics are often (surprisingly) repeated and the overall structure of the book doesn't seem to make sense at times. But if you read each chapter more or less independently, this isn't a serious problem. The iDA tool that comes with the text is a trial, 180-day version, and it is unlikely that the average reader will want to spend $5,000 to purchase a license for the commercial product after the six month trial is up. So you should buy the book knowing ahead of time that after a while, the iDA tool will no longer be available to go back over the examples or exercises.
6 of 6 people found the following review helpful
A very good choice for learning data mining concepts with minimal resources
12 Oct 2005
Format:Paperback
The particularity of this book is that it is more accessible to read than most of data mining books, which in general require some maths/statistics/computing background.
The book is not written in the best way from the point of view of a data mining expert, as for instance sometimes a theme is recurrent in the text, but it is not obvious to explain data mining concepts using minimal previous knowledge in computing/maths/statistics.
A second important positive aspect is that the book comes with a software (IDA) running under Excel, which can be used to illustrate the techniques presented in the book (BTW a new version of the software is freely available to download, regularly). This is not the case with most of the data mining books. So if you wish to learn the basics of data mining with minimal or no previous resources (good maths/computing background and access to expensive data mining software) then this is a very good choice.
The book is not written in the best way from the point of view of a data mining expert, as for instance sometimes a theme is recurrent in the text, but it is not obvious to explain data mining concepts using minimal previous knowledge in computing/maths/statistics.
A second important positive aspect is that the book comes with a software (IDA) running under Excel, which can be used to illustrate the techniques presented in the book (BTW a new version of the software is freely available to download, regularly). This is not the case with most of the data mining books. So if you wish to learn the basics of data mining with minimal or no previous resources (good maths/computing background and access to expensive data mining software) then this is a very good choice.
12 of 15 people found the following review helpful
Unique approach and many insights
16 Feb 2003
Format:Paperback
Using an Excel-based tool to provide a hands-on approach, this book covers diverse areas in data mining from statistical patterns to data warehousing to the semantic web. Easy to read and with many insights.
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