Session:

Knowledge Representation, Decision Support and Expert Systems
Tuesday, March 02, 2004, 11.30 – 11.50

Session Chair:
Vice Chair:

A. Dobnikar
M. Savoji

Paper Title:

Optimisation of Pattern Mining : A New Method Founded on Database Transposition

Author(s):

F. Rioult, Université de Caen, France
B. Crémilleux, Université de Caen, France

Abstract:

Looking in a database for interesting patterns of attributes (the columns) or groups of objects (lines) that verify some properties is a classic task in data mining, today well mastered. Nevertheless, some difficult contexts such as the data provided by gene analysis remain intractable, because of a disproportionate number of attributes, compared to the number of objects. In these conditions, it is naturally tempting to transpose the matrix of data to carry out more efficiently the pattern mining. This article exposes this new method and shows its interest but also the difficulties to solve so that this approach is fruitful. With using the Galois connection, the extraction achieved in the transposed base allows to infer results on the initial data. We detail the contributions of this practice on data containing a big number of attributes, such as data of genome, as well as its possible application to the mining under monotonous constraint and the obtaining of the totality of the closed patterns.