Scalable dynamic self-organising maps for mining massive textual data

Authors:
Yu Zheng Zhai;Arthur Hsu;Saman K. Halgamuge
Affiliations:
Department of Mechanical and Manufacturing Engineering, University of Melbourne, Victoria, Australia;Department of Mechanical and Manufacturing Engineering, University of Melbourne, Victoria, Australia;Department of Mechanical and Manufacturing Engineering, University of Melbourne, Victoria, Australia
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 7
Cited 1

Fast and effective text mining using linear-time document clustering

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Organizing Maps

Self-Organizing Maps
The Grid 2: Blueprint for a New Computing Infrastructure

The Grid 2: Blueprint for a New Computing Infrastructure
Mining massive document collections by the WEBSOM method

Information Sciences: an International Journal - Special issue: Soft computing data mining
Controlling the spread of dynamic self-organising maps

Neural Computing and Applications
Dynamic self organizing maps for discovery and sharing of knowledge in multi agent systems

Web Intelligence and Agent Systems
Dynamic self-organizing maps with controlled growth for knowledge discovery

IEEE Transactions on Neural Networks

Scalable data clustering: a sammon's projection based technique for merging GSOMs

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

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.