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MERLOT II




        

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Data Mining: An Introduction

        

Data Mining: An Introduction

Logo for Data Mining: An Introduction
This material is an introductory tutorial on data mining. The site is part of The About network which consists of hundreds of sites organized into 23 channels covering more than 50,000 subjects. The site offers relevant links, how-to's, forums, and answers to questions in addition to the tutorial. The tutorial covers the business motivations for data mining and briefly discusses the two popular data mining techniques, regression analysis and classification analysis.
Material Type: Tutorial
Technical Format: HTML/Text
Date Added to MERLOT: December 31, 2001
Date Modified in MERLOT: November 15, 2010
Author:
Send email to databases.guide@about.com
Submitter: Emrah Orhun

Quality

About

Primary Audience: College General Ed, Professional
Mobile Compatibility: Not specified at this time
Language: English
Cost Involved: no
Source Code Available: no
Accessiblity Information Available: no
Creative Commons: unsure

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Avatar for Drew Still
6 years ago

Drew Still (Researcher)

Excellent, easy to use and very informative

Avatar for David Brown
10 years ago

David Brown (Staff)

A breezy, informal, and ultimately shallow introduction to data mining. Although
it refers to a few 'case studies' (not case studies in a formal sense but
rather simply refers to situations in which data mining has been used), it does
not talk about any of them in sufficient detail to actually illustrate what data
mining is about. It takes less than five minutes to read the actual article,
but you could spend hours reading through all of the articles it links to. Some
of these articles are detailed discussions of data mining or statistical methods
related to data mining, some are links to companies that provide data mining
products, while others are more whimsical (Multiple Regression with Ren &
Stimpy).


As an actual introduction to data mining it is inadequate. As an introduction to
where you can get an introduction to data mining it is adequate.

Used in course