Goodman-Kruskal measure associated clustering for categorical data
by Wenxue Huang; Yuanyi Pan; Jianhong Wu
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 4, No. 4, 2012

Abstract: Motivated by business interest of return on investment (ROI) in marketing, we develop a conceptual clustering algorithm for categorical data with a response variable based on a variation to Goodman-Kruskal measure. The key to this algorithm is an implicitly cost-effective dissimilarity measure derived from a probabilistic association rule between the response and the explanatory scenarios. Applications to a real dataset FAMEX96 illustrate how useful information can be mined from marketing data using this dissimilarity measure.

Online publication date: Sat, 23-Aug-2014

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 Data Mining, Modelling and Management (IJDMMM):
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