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

Paweł Boiński; Maciej Zakrzewicz

doi:10.1007/978-3-642-32741-4_3

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Title

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

Authors

Paweł Boiński (WI) ^{[ 1 ][ P ]}
Maciej Zakrzewicz (WI) ^{[ 1 ][ P ]}

^{[ 1 ]} Instytut Informatyki, Wydział Informatyki, Politechnika Poznańska | ^{[ P ]} employee

Year of publication

2013

Chapter type

paper

Publication language

english

Abstract

EN 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.

Pages (from - to)

19 - 30

DOI

10.1007/978-3-642-32741-4_3

URL

https://link.springer.com/chapter/10.1007/978-3-642-32741-4_3

Book

Advances in databases and information systems

Presented on

16th East-European Conference on Advances in Databases and Information Systems, ADBIS 2012, 17-21.09.2012, Poznan, Poland

Publication indexed in

WoS (15)

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