PatZip: pattern-preserved spatial data compression

Authors:
Yu Qian;Kang Zhang;D. T. Huynh
Affiliations:
Department of Computer Science, The University of Texas at Dallas, Richardson, TX;Department of Computer Science, The University of Texas at Dallas, Richardson, TX;Department of Computer Science, The University of Texas at Dallas, Richardson, TX
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

This paper presents a compression method, PatZip, to improve the efficiency of spatial pattern mining methods. PatZip can avoid overcompression and stop automatically before pattern is destroyed. Compared with existing compression methods, PatZip is deterministic and its result is reproducible, and original data can be easily recovered. The compression process is data-driven and parameter-free, and requires only O(nlogn) time for n data points.