Data Mining Defined

Data Mining Defined

Stephan Kudyba (Economic Consultant, USA) and Richard Hoptroff (Consultant, The Netherlands)
Copyright: © 2001 |Pages: 20
DOI: 10.4018/978-1-930708-03-7.ch002
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Abstract

Over the years, the term data mining has been connected to various types of analytical approaches. In fact, just a few years ago, let’s say prior to 1995, many individuals in the software industry and business users as well, often referred to OLAP as a main component of data mining technology. More recently however, this term has taken on a new meaning and one which will most likely prevail for years to come. As we mentioned in the previous chapter, data mining technology encompasses such methodologies as clustering, classification and segmentation, association, neural networks and regression as the main players in this space. Other analytical processes which are related to mining, as defined in this work, include such methodologies as Linear Programming, Monte Carlo analysis and Bayesian methodologies. In fact, depending on who you ask, these techniques may actually be considered part of the data mining spectrum since they are grounded in mathematical techniques applied to historical data. The focus of this work however, revolves around the former more core approaches. Regardless of the type of methodology, data mining has taken its roots from traditional analytical techniques. Enhancements in computer processing, (e.g., speed and processing power) has enabled a wider diffusion of more complex techniques to become more automated and user friendly and have evolved to the state of our current data mining.

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Table of Contents
Acknowledgments
Chapter 1
Stephan Kudyba, Richard Hoptroff
The world of commerce has undergone a transformation since the early 1990s, which has increasingly included the utilization of information... Sample PDF
An Introduction to Information Technology and Business Intelligence
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Chapter 2
Data Mining Defined  (pages 23-42)
Stephan Kudyba, Richard Hoptroff
Over the years, the term data mining has been connected to various types of analytical approaches. In fact, just a few years ago, let’s say prior to... Sample PDF
Data Mining Defined
$37.50
Chapter 3
Stephan Kudyba, Richard Hoptroff
The previous chapters have given you some background on the core components of corporate IT systems along with software technology that promotes... Sample PDF
Steps to Success for the Mining Process
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Chapter 4
Stephan Kudyba, Richard Hoptroff
Forecasting and “what if” mining generally incorporates the application of regression and neural network methodologies. In certain cases, for more... Sample PDF
Essential Mining Approaches to Problem Solving
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Chapter 5
Stephan Kudyba, Richard Hoptroff
Two core business strategies throughout the realm of commerce, which take their root from traditional economic theory, involve the incorporation of... Sample PDF
A Closer Look at Marketing/Advertising, Promotions and Pricing Policies Using Econometric Based Modeling
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Chapter 6
Stephan Kudyba, Richard Hoptroff
I recently read an article about words and terms that evolved exclusively from the American culture. This piece traced the history of American-born... Sample PDF
Turning Your Brick and Mortar into a Click and Mortar
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Chapter 7
Stephan Kudyba, Richard Hoptroff
The previous chapter introduced the evolution of the information economy as it addressed the progress of commerce from “brick and mortar” to “click... Sample PDF
Improving the Web Experience through Real-Time Analysis (A Market Basket Approach)
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Chapter 8
Stephan Kudyba, Richard Hoptroff
Up to now we have presented the fundamental building blocks to understanding the concept of data mining and addressed the prevailing applications... Sample PDF
Bringing It All Together (Data Mining on an Enterprise Level)
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Chapter 9
Stephan Kudyba, Richard Hoptroff
Predicting the future is always a difficult task, of course that depends on how far into the future one attempts to delve. With regard to data... Sample PDF
What the Future Holds for Data Mining
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