Conference Proceedings

Scalable dynamic self-organising maps for mining massive textual data

Yu Zheng Zhai, Arthur Hsu, Saman K Halgamuge, I King (ed.), J Wang (ed.), L Chan (ed.), DL Wang (ed.)

NEURAL INFORMATION PROCESSING, PT 3, PROCEEDINGS | SPRINGER-VERLAG BERLIN | Published : 2006

Abstract

Traditional text clustering methods require enormous computing resources, which make them inappropriate for processing large scale data collections. In this paper we present a clustering method based on the word category map approach using a two-level Growing Self-Organising Map (GSOM). A significant part of the clustering task is divided into separate subtasks that can be executed on different computers using the emergent Grid technology. Thus enabling the rapid analysis of information gathered globally. The performance of the proposed method is comparable to the traditional approaches while improves the execution time by 15 times. © Springer-Verlag Berlin Heidelberg 2006.

University of Melbourne Researchers