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)


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.


Spatial Feature Spatial Object Real World Dataset Neighbor Relationship Spatial Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

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