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Studying the Impact of Partition on Data Reduction for Very Large Spatio-temporal Datasets

Authors:
Nhien An Le Khac
Martin Bue
M-Tahar Kechadi

Keywords: spatio-temporal datasets; data reduction; data partition; density-based clustering; shared nearest neighbours

Abstract:
Nowadays, huge amounts of data are being collected with spatial and temporal components from sources such as metrological, satellite imagery etc. Efficient visualisation as well as discovery of useful knowledge from these datasets is therefore very challenging and becoming a massive economic need. Data Mining has emerged as the technology to discover hidden knowledge in very large amounts of data. Furthermore, data mining techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. As a consequence, instead of dealing with a large size of raw data, we can use these representatives to visualise or to analyse without losing important information. Recently, we proposed a new approach based on different clustering techniques for data reduction to help analyse large spatio-temporal data. This approach is based on the partition of huge datasets due to the memory constraint. In this paper, we evaluate the impact of various numbers of partitions on our data reduction approach

Pages: 41 to 46

Copyright: Copyright (c) IARIA, 2011

Publication date: January 23, 2011

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-115-1

Location: St. Maarten, The Netherlands Antilles

Dates: from January 23, 2011 to January 28, 2011