The Community for Technology Leaders
Green Image
Issue No. 07 - July (2014 vol. 26)
ISSN: 1041-4347
pp: 1562-1574
Guimei Liu , Data Analytics Department, Institute for Infocomm Research, Singapore
Haojun Zhang , Department of Computer Science, National University of Singapore, Singapore
Limsoon Wong , Department of Computer Science, National University of Singapore, Singapore
ABSTRACT
Frequent pattern mining often produces an enormous number of frequent patterns, which imposes a great challenge on visualizing, understanding and further analysis of the generated patterns. This calls for finding a small number of representative patterns to best approximate all other patterns. In this paper, we develop an algorithm called MinRPset to find a minimum representative pattern set with error guarantee. MinRPset produces the smallest solution that we can possibly have in practice under the given problem setting, and it takes a reasonable amount of time to finish when the number of frequent closed patterns is below one million. MinRPset is very space-consuming and time-consuming on some dense datasets when the number of frequent closed patterns is large. To solve this problem, we propose another algorithm called FlexRPset, which provides one extra parameter K to allow users to make a trade-off between result size and efficiency. We adopt an incremental approach to let the users make the trade-off conveniently. Our experiment results show that MinRPset and FlexRPset produce fewer representative patterns than RPlocal-an efficient algorithm that is developed for solving the same problem.
INDEX TERMS
data mining,
CITATION

G. Liu, H. Zhang and L. Wong, "A Flexible Approach to Finding Representative Pattern Sets," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 7, pp. 1562-1574, 2014.
doi:10.1109/TKDE.2013.27
621 ms
(Ver 3.3 (11022016))