Abstract
Collocation pattern mining is one of the latest data mining techniques applied in Spatial Knowledge Discovery. We consider the problem of executing collocation pattern queries in a limited memory environment. In this paper we introduce a new method based on iCPI-tree materialization and a spatial partitioning to efficiently discover collocation patterns. We have implemented this new solution and conducted series of experiments. The results show a significant improvement in processing times both on synthetic and real world datasets.
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Boinski, P., Zakrzewicz, M. (2013). Partitioning Approach to Collocation Pattern Mining in Limited Memory Environment Using Materialized iCPI-Trees. In: Morzy, T., Härder, T., Wrembel, R. (eds) Advances in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32741-4_3
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DOI: https://doi.org/10.1007/978-3-642-32741-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32740-7
Online ISBN: 978-3-642-32741-4
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