Clustering massive high dimensional data with dynamic feature maps

Authors:
Rasika Amarasiri;Damminda Alahakoon;Kate Smith-Miles
Affiliations:
Clayton School of Information Technology, Monash University, VIC, Australia;Clayton School of Information Technology, Monash University, VIC, Australia;School of Engineering and Information Technology, Deakin University, Burwood, VIC, Australia
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

This paper presents an algorithm based on the Growing Self Organizing Map (GSOM) called the High Dimensional Growing Self Organizing Map with Randomness (HDGSOMr) that can cluster massive high dimensional data efficiently. The original GSOM algorithm is altered to accommodate for the issues related to massive high dimensional data. These modifications are presented in detail with experimental results of a massive real-world dataset.