WIT Press


Discovering Graph Structures In High Dimensional Spaces

Price

Free (open access)

Paper DOI

10.2495/DATA000221

Volume

25

Pages

9

Published

2000

Size

866 kb

Author(s)

V. Dubois & M. Quafafou

Abstract

This paper introduces a new idea to face this problem by adding the notion of func- tional dependency to the context of minimum encoding inference. Our analysis is based on a local discovery of relations between binary features. We present a very effective expression of class model local note, wich can thus be computed without knowledge of any parameter value. The first method of data analysis described in this paper (also called crude method) is the direct application of our expression of the model class local note, and produce pieces of knowledge discovered in the data, organized in a graph. 1 Introduction Discovering complex structures in a database is an emerging research area which deals with a hard task. Most of the proposed solutions are developped in a proba- bilistic context which leads to discovering Bayesian Networks traducing causality phenomena.

Keywords