White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Principles of employing a self-organizing map as a frequent itemset miner

Baez-Monroy, V.O. and O’Keefe, S. (2005) Principles of employing a self-organizing map as a frequent itemset miner. In: Artificial Neural Networks: Biological Inspirations. Porceedings of ICANN 2005. ICANN 2005: 15th International Conference, September 11-15, 2005, Warsaw, Poland. Lecture Notes in Computer Science, Part I (3696). Springer-verlag , Berlin / Heidelberg , pp. 363-370. ISBN 978-3-540-28752-0

Full text not available from this repository.

Abstract

This work proposes a theoretical guideline in the specific area of Frequent Itemset Mining (FIM). It supports the hypothesis that the use of neural network technology for the problem of Association Rule Mining (ARM) is feasible, especially for the task of generating frequent itemsets and its variants (e.g. Maximal and closed). We define some characteristics which any neural network must have if we would want to employ it for the task of FIM. Principally, we interpret the results of experimenting with a Self-Organizing Map (SOM) for this specific data mining technique.

Item Type: Proceedings Paper
Academic Units: The University of York > Computer Science (York)
Depositing User: York RAE Import
Date Deposited: 08 Apr 2009 16:15
Last Modified: 08 Apr 2009 16:15
Published Version: http://dx.doi.org/10.1007/11550822_57
Status: Published
Publisher: Springer-verlag
Identification Number: 10.1007/11550822_57
URI: http://eprints.whiterose.ac.uk/id/eprint/5657

Actions (login required)

View Item View Item