Partitioning Approach to Collocation Pattern Mining in Limited Memory Environment Using Materialized iCPI-Trees

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

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