By Topic

Mining association rules from data with hybrid attributes based on immune genetic algorithm

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

1 Author(s)
Guangjun Yang ; Mech. Electron. Eng. Dept., Dezhou Univ., Dezhou, China

Extracting association rules from data with both discrete and continuous attributes is an important problem in KDD. A new model of immune genetic algorithm is formulated for solving this problem. This algorithm uses three-segment chromosomes, integrating the discretization, attributes reduction and mining association rules. And immune mechanism is introduced into genetic algorithm to avoid premature phenomenon and improve the efficiency of GA. The results of experiments prove the correctness and validity of the algorithm.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on  (Volume:3 )

Date of Conference:

10-12 Aug. 2010