Decision trees for filtering large databases of graphs
by Christophe Irniger, Horst Bunke
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 3, No. 3/4, 2007

Abstract: Graphs are a powerful representation formalism for structural data. They are, however, very expensive from the computational point of view. In pattern recognition and intelligent information processing it is often necessary to match an unknown sample against a database of candidate patterns. In this process the size of the database is introduced as an additional factor into the overall complexity of the matching process. To reduce the influence of that factor, an approach based on machine learning techniques is proposed in this paper. Firstly, graphs are represented using feature vectors. Then, based on these vectors, a decision tree is built to index the database. At runtime the decision tree allows one to eliminate a number of graphs from the database to reduce possible matching candidates.

Online publication date: Thu, 28-Jun-2007

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com