Partitioning approach to collocation pattern mining in limited memory environment using materialized iCPI-trees

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. © 2013 Springer-Verlag.