By Topic

An adaptive semi-supervised fuzzy clustering algorithm based on objective function optimization

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

2 Author(s)
Macario, V. ; Centro de Inf. (CIn), Univ. Fed. de Pernambuco (UFPE), Recife, Brazil ; de Carvalho, F.D.A.T.

Semi-supervised learning uses large amount of unlabeled data, combined with the labeled data, to guide the learning process. This paper introduces a new semi-supervised clustering algorithm based on an adaptive distance. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive distance allowing the construction of partitions in ellipsoids format, in addition to spherical shape generated by the Euclidean distance. Experiments with real and synthetic data sets show the usefulness of the proposed method by comparing with others adaptive and non-adaptive semi-supervised clustering algorithms in a clustering task.

Published in:

Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on

Date of Conference:

10-15 June 2012