Collocation pattern mining in a limited memory environment using materialized iCPI-tree

Abstract

We consider the problem of executing collocation pattern queries in limited memory environments. Our experiments show that if the memory size is not sufficient to hold all internal data structures used by the iCPI-tree algorithm, its performance decreases dramatically. We present a new method to efficiently process collocation pattern queries using materialized, improved candidate pattern instance tree. We have implemented and tested the aforementioned solution and shown that it can significantly improve the performance of the iCPI-tree algorithm. © 2012 Springer-Verlag.