GAM: a guidance enabled association mining environment
by Aaron Ceglar, John F. Roddick
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 2, No. 1, 2007

Abstract: The quality of data mining results is largely dependent on the ability to accommodate context and user requirements within the mining process. This is done effectively within the pre-processing and presentation stages, however the analysis (or mining) stage remains relatively autonomous and opaque with user input commonly limited to parameter setting. There is, at present, no direct manipulation of the analysis stage which results in the analysis of the domain space being statically constrained. This reduces the quality of results and increases the time needed for analysis. This paper presents a guided association mining environment, GAM, that enhances user-computer synergy by incorporating the user at fine level of granularity within the analysis stage. GAM extends the current state of the art and is based upon a generic guided knowledge discovery environment.

Online publication date: Sat, 31-Mar-2007

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com