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Predictive Data Mining: A Practical Guide (The Morgan Kaufmann Series in Data Management Systems), by Sholom M. Weiss, Nitin Indurkhya
PDF Ebook Predictive Data Mining: A Practical Guide (The Morgan Kaufmann Series in Data Management Systems), by Sholom M. Weiss, Nitin Indurkhya
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The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles—and their practical manifestations—in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.
Note: If you already own Predictive Data Mining: A Practical Guide, please see ISBN 1-55860-477-4 to order the accompanying software. To order the book/software package, please see ISBN 1-55860-478-2.
+ Focuses on the preparation and organization of data and the development of an overall strategy for data mining.
+ Reviews sophisticated prediction methods that search for patterns in big data.
+ Describes how to accurately estimate future performance of proposed solutions.
+ Illustrates the data-mining process and its potential pitfalls through real-life case studies.
- Sales Rank: #1932517 in eBooks
- Published on: 1997-12-08
- Released on: 1997-12-08
- Format: Kindle eBook
Amazon.com Review
Data mining is a hot technology, and this short, authoritative guide shows how it works and why it is gaining ground in the worlds of finance, manufacturing, marketing, and health care. The book begins by exploring the links between "big data"--the data warehouse built up of multiple databases--and traditional statistics. (The authors defend the methods of big data against traditional statistics, which has usually relied on smaller samples. However, they also look at the sources of error in both disciplines.)
The authors then look at the nuts and bolts of the data-mining process. They show how data must be prepared--sometimes reduced--in order to be manageable, and they define the important features. They show how the actual analysis of data mining can be as simple as adding up scores for selected features or how it can use statistical methods or even neural networks. (For some problems, the features themselves aren't known ahead of time; data mining can be used to discover these features automatically.) The authors then discuss how to interpret the results of analysis so that predictions can be made for new cases based on old ones.
The book concludes with short scenarios of how data mining can be applied, with examples drawn from manufacturing, health care, marketing, and publishing. The authors show the strengths--and limits--of data mining and argue that faster hardware and greater database storage capabilities will make this technology more widely used. Though it is written by two researchers in the field, Predictive Data Mining is suitable for general readers who are interested in the topic. --Richard V. Dragan
Review
"I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians and data miners."
—Emanuel Parzen, Distinguished Professor of Statistics, Texas A&M University
From the Back Cover
Note: If you already own Predictive Data Mining: A Practical Guide, please click here to order the software only. To order the book without software, please click here.
The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles-and their practical manifestations-in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.
- Focuses on the preparation and organization of data and the development of an overall strategy for data mining.
- Reviews sophisticated prediction methods that search for patterns in big data.
- Describes how to accurately estimate future performance of proposed solutions.
- Illustrates the data-mining process and its potential pitfalls through real-life case studies.
A state-of-the-art data-mining software kit accompanies the book. The software, which is delivered through a special web site, is a collection of routines for efficient mining of big data. Both classical and the more computationally expensive state-of-the-art prediction methods are included. Using a standard spreadsheet data format, this kit implements all of the data-mining tasks described in the book. The software is available for Windows 95/NT and Unix platforms (no need to specify when ordering).It presents an excellent
perspective on the theory and practice of data mining. It can help
educate statisticians to build alliances between statisticians and
data miners."
Emanuel Parzen
Distinguished Professor of Statistics, Texas A&M University
Most helpful customer reviews
9 of 9 people found the following review helpful.
Reasonably good introduction
By A Customer
This isn't a bad book to pick up if you want to find out what data mining is about. I did, and it served as a good introduction.
For those of you who, like me, don't know what this is about, let me try to summarize. For years, organizations have been collecting a lot of information, via computer, just to run their business. For legal and business reasons, they have had to hold on to it, long past what they considered to be its useful life. But other than just storing it, what good is it?
Well, someone decided it could be used to answer questions about the business. Enter the data warehouse. The idea is to take all this old data, clean it up, and put it in a large database. Then the data can be mined for information.
There are two functions of data mining. One is to answer questions about the business. The other is to discover new knowledge about the business that you did not even have the sense to put in the form of a question. Everything from simple statistics to neural nets, genetic algorithms, and evolutionary programs can be used to mine the data.
Like any other science, this can be used for good purposes (what's the main reason homeless people become homeless), or bad (who's most likely to buy the Brooklyn Bridge). It can be used in many areas of science, although I suspect it will mostly be used by businesses trying to take marketing where no man has gone before.
The book itself is mostly prose, so it's an easy read, although it does require some computer knowledge. The more technical sections (like k-means and entropy clustering) are awkwardly written. But this does not detract from the overall effectiveness of the book.
If you're a manager whose boss just told you to head up the data warehouse, and you don't have a clue what he's talking about, this wouldn't be a bad book to get.
I give it a 7. It's easy to dance to.
5 of 5 people found the following review helpful.
Excellent book (but poor software)
By A Customer
I found this book to be an excellent description of the entire life cycle of data mining. It does not attempt to give detailed descriptions of the technical features of various methods, instead referring to relevant books and articles.
Those who are interested in undertaking data mining will need to obtain some of the mentioned references, a book that includes technical details, or a data mining software package.
The methods listed in the book have been implemented in some software that is separately available. My only disappointment with Predictive Data Mining is with this software - it is so poorly documented (input and output) that it is virtually useless.
In summary, those wanting a "managerial overview" of data mining will certainly gain it from this book. Those wanting actually to do data mining will need a technical book or some software (but not this book's software).
5 of 5 people found the following review helpful.
Excellent introduction
By Amrit B. Tiwana
I felt that this was an excellent intorduction to an area otherwise overcrowded with untested and semi validated "methodologies". I loved the fact that this title is not vendor specific (unlike some other titles trying to sell you tools or what!). Seeing the MKP name on it is reassuring. Would'nt hesitate to recommend this one!
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