By Topic

Value of information gained from data mining in the context of information sharing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Saygin, Y. ; Fac. of Eng. & Natural Sci., Sabanci Univ., Istanbul, Turkey ; Reisman, A. ; YunTong Wang

This paper uses a game-theoretic framework to suggest the fair value for information extracted via data mining and shared between two retail-market competitors. For mutual benefit, the two players each owning a privileged information set (a collection of data or database) may want to share or pool all or part of the information contained within their respective databases. Assume that each player is equipped with a data mining technique which extracts information from the data. We first model the information sharing as a cooperative game. Then, we use results from the cost sharing literature to provide information sharing methods when data can be quantified either as discrete or as continuous variables. In the latter case, we provide a means for obtaining decision rules for pricing shared information.

Published in:

Engineering Management, IEEE Transactions on  (Volume:51 ,  Issue: 4 )