Self-Organizing-Map-Based metamodeling for massive text data exploration

Authors:
Kin Keung Lai;Lean Yu;Ligang Zhou;Shouyang Wang
Affiliations:
College of Business Administration, Hunan University, Changsha, China;Department of Management Sciences, City University of Hong Kong, Hong Kong;Department of Management Sciences, City University of Hong Kong, Hong Kong;College of Business Administration, Hunan University, Changsha, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 4
Cited 0

On Parallel Processing Systems: Amdahl's Law Generalized and Some Results on Optimal Design

IEEE Transactions on Software Engineering
Self-Organizing Maps

Self-Organizing Maps
A Multilingual Text Mining Approach Based on Self-Organizing Maps

Applied Intelligence
Self organization of a massive document collection

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this study, we describe the use of the self-organizing map (SOM) as a metamodeling technique to design a parallel text data exploration system. Firstly, the large textual collections are divided into various small data subsets. Based on the different subsets, different unitary SOM models, i.e., base models, are then trained for word clustering map. In this phase, different SOM models are implemented in parallel to gain greater computational efficiency. Finally, a SOM-based metamodel can be produced to formulate a text category map through learning from all base models. For illustration the proposed metamodel is applied to a massive text data collection.