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Author: Michael Geatz,Richard Roiger
ISBN13: 978-0201741285
Title: Data Mining: A Tutorial Based Primer
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ePUB size: 1879 kb
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Language: English
Category: Computer Science
Publisher: Pearson; 1st edition (October 6, 2002)
Pages: 408

Data Mining: A Tutorial Based Primer by Michael Geatz,Richard Roiger

Richard J. Roiger is a professor of computer science at Minnesota State University, Mankato and a senior software engineer for Information Acumen Corporation (ww. nfoacumen. Richard received a P. degree in Computer Science from the University of Minnesota in 1991. He is a member of the American Association of Artificial Intelligence, the Association for Computing Machinery and IEEE.

The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Government works Printed on acid-free paper Version Date: 20161025 International Standard.

Richard J.

Richard Roiger, Michael Geatz. Data Mining: A Tutorial Based Primer. Richard Roiger, Michael Geatz. Data Mining: A Tutorial Based Primer Richard Roiger, Michael Geatz 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.

2nd ed. - CRC Press, 2016. 512 p. - (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series). The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem.

Richard Roiger, Minnesota State University. Michael Geatz, Information Acumen Corporation. 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 . Roiger, Michael W. Geatz. Addison-Wesley Publishing 2003. Updated: July 15, 2012. Supplements: iData Analyzer Download. Using WEKA with Data Mining A Tutorial-Based Primer. PowerPoint Slides (Includes slides on microarray data mining). More Datasets (Now includes two microarray datasets). Neural Networks (New GUI Interface!)

2 The Course Book Data Mining: A Tutorial Based Primer by Richard . oiger, Michael Geatz. 3 . Data Mining: A Definition The process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data

Contributor(s): Geatz, Michael. Publisher: Boston : Addison Wesley, c2003Description: 1 v. (various pagings) : ill. ; 24 cm. + 1 CD-ROM (4 3/4 i. Subject(s): Data mining Veri madenciliği DDC classification: 00. Tags from this library: No tags from this library for this title. Genel Koleksiyon, Main Collection.

Author: Richard J. Roiger. Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustrations. In addition, his tutorials in Weka software provide excellent grounding for students in comprehending the underpinnings of Machine Learning as applied to Data Mining.

Primer on data mining provides an introduction to the principles and techniques for extracting information, from a business-minded executive. Data sets from the CD-ROM are used in examples and exercises. Softcover.
Reviews: 7
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.
it's ok. Concepts are still solid, software is out of date w/ technology. Not sure why colleges still use this. Or even use any books now that many materials and videos (like the ones the very teachers at college make and post) are online. Maybe it's time to give up the college campus book store and put in a few more classrooms.
This book is touted as an intro to Data Mining, and while it does cover most of the intro topics you'd want to learn about, it seems to cover some in more depth. This was a text book for an Information Organization/Retrieval course I took, but our instructor subsidized it pretty heavily with other sources and her own notes. Worked very well when used in a classroom setting.
What can I say.... It's a book on Data Mining....
Learned a lot.
When I bought this book, I knew nothing about data mining. Unfortunately, this book glossed over the topics I knew least about and spent a depressing amount of time on stuff anyone should have learned by junior high. They introduced 188 "Key Terms" in a book that's only 350 pages long. In chapter 1 they give definitions for words like "fact", "hypothesis", etc. Yet by chapter 5 they start flinging the symbols for attribute standard deviation at you with no explanation or warning. So I'm not sure who they think will be reading their book...but from what I can tell, they assume their target audience can handle advanced algebra with ease but may need a definition of "the scientific method".

They also spend quite a bit of time walking you through the Excel PivotTable creation wizard and other such fluff. They carefully instruct the reader that dragging and dropping is accomplished through use of the mouse, and that you should drop columns into the area marked 'Drop Column Fields Here'.

On the upside, I do know a bit more about data mining now. I don't feel that I could run right out and get a job, but at least when I start reading another book I'll have an idea of what the terms and concepts are.

So I suppose if you're good at statistics, have never taken a basic science course and have poor computer skills, this book is for you.
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.
This can now be considered an excellent course text or professional reference for Data Mining and Predictive Analytics since the author has now issued changes to support open source WEKA Data Mining Software as an alternative to the iData Analyzer Software on CD included with the text which was problematic in certain situations. Students in my Data Mining, Predictive Analytics, and Data Warehousing courses have enjoyed learning Data Mining with this book in conjunction with WEKA software. The newer WEKA formatted sample files now available are very helpful also. This book is an excellent intro to Data Mining Concepts which is particularly useful before beginning work with Oracle Data Miner or SQL Server Analysis Services Data Mining Projects.