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By Alan Woodley

Dec

Publication Type: Journal paper
State/Country: Australia
Other Authors: Ling-Xiang Tang, Shlomo Geva, Richi Nayak and Timothy Chappell
CRC Contact: QUT and CRCSI

Description: Abstract — Clustering can help to make large datasets more manageable by grouping together similar objects. However, most clustering approaches are unable to scale to very large datasets (eg. more than 10 million objects). The K-Tree is a data structure and clustering algorithm that has proven to be scalable with large streaming datasets. Here, we apply the K-Tree to spatial data (satellite images) and extend from a single threaded to a multicore environment. We show that the K-Tree is able to cluster larger datasets more efficiently than baseline approaches.

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