Collocation Pattern Mining in a Limited Memory Environment Using Materialized iCPI-Tree-Reference-Cited by-同舟云学术

Collocation Pattern Mining in a Limited Memory Environment Using Materialized iCPI-Tree

Published:2012 Issue: Volume: Page:279-290
ISSN:0302-9743
Container-title:Data Warehousing and Knowledge Discovery
language:
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Author:

Boinski Pawel,Zakrzewicz Maciej

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-32584-7_23.pdf

Reference15 articles.

1. Openstreetmap, http://www.openstreetmap.org

2. Agrawal, R., Imieliński, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. SIGMOD Rec. 22(2), 207–216 (1993)

3. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)

4. Boinski, P., Zakrzewicz, M.: Hash Join Based Spatial Collocation Pattern Mining. Foundations of Computing and Decision Sciences 36(1), 3–15 (2011)

5. Boinski, P., Zakrzewicz, M.: Memory Constraints in the Collocation Pattern Mining. Tech. rep., Poznan University of Technology (2012)

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