By Topic

Solving multiobjective clustering using an immune-inspired 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
$33 $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

4 Author(s)
Maoguo Gong ; Institute of Intelligent Information Processing, Xidian University, Xi¿an 710071, China ; Lining Zhang ; Licheng Jiao ; Shuiping Gou

In this study, we introduced a novel multiobjective optimization algorithm, Nondominated Neighbor Immune Algorithm (NNIA), to solve the multiobjective clustering problems. NNIA solves multiobjective optimization problems by using a nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators and elitism. The main novelty of NNIA is that the selection technique only selects minority isolated nondominated individuals in current population to clone proportionally to the crowding-distance values, recombine and mutate. As a result, NNIA pays more attention to the less-crowded regions in the current trade-off front. The experimental results on seven artificial data sets with different manifold structure and six real-world data sets show that the NNIA is an effective algorithm for solving multiobjective clustering problems, and the NNIA based multiobjective clustering technique is a cogent unsupervised learning method.

Published in:

2007 IEEE Congress on Evolutionary Computation

Date of Conference:

25-28 Sept. 2007