By Topic

Topology Description for Data Distributions Using a Topology Graph With Divide-and-Combine Learning Strategy

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)
Ming-Ming Sun ; Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol. ; Jian Yang ; Jing-Yu Yang

The topologies of data distributions are very important for data description. Usually, it is not easy to find a description that can give us an intuitional understanding of the topologies for general distributions. In this paper, a novel concept, a topology graph, is proposed as a description for the principal topology of data distribution. The topology graph builds a one-to-one correspondence between the principal topology of the distribution and the topology itself: annularity features of the principal topology correspond to the loops of the graph, and the divarification features correspond to the branches of the graph. In general, the topology graph can be considered as the skeleton of the data distribution. A divide-and-combine learning strategy is developed to find the topology graphs for general data distributions. The learning strategy is focused on the constrained local description learning and automatic topology generation. Following the learning strategy, a cluster growing algorithm is developed. Experimental results on both artificial datasets and real-world applications show good performance of the proposed algorithm

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:36 ,  Issue: 6 )