Please use this identifier to cite or link to this item: https://doi.org/10.1145/2339530.2339543
Title: Finding minimum representative pattern sets
Authors: Liu, G. 
Zhang, H.
Wong, L. 
Keywords: frequent pattern summarization
representative patterns
Issue Date: 2012
Source: Liu, G.,Zhang, H.,Wong, L. (2012). Finding minimum representative pattern sets. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 51-59. ScholarBank@NUS Repository. https://doi.org/10.1145/2339530.2339543
Abstract: Frequent pattern mining often produces an enormous number of frequent patterns, which imposes a great challenge on understanding and further analysis of the generated patterns. This calls for finding a small number of representative patterns to best approximate all other patterns. An ideal approach should 1) produce a minimum number of representative patterns; 2) restore the support of all patterns with error guarantee; and 3) have good efficiency. Few existing approaches can satisfy all the three requirements. In this paper, we develop two algorithms, MinRPset and FlexRPset, for finding minimum representative pattern sets. Both algorithms provide 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. FlexRPset is developed based on MinRPset. It provides one extra parameter K to allow users to make a trade-off between result size and efficiency. 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. FlexRPset can be slightly faster than RPlocal when K is small. © 2012 ACM.
Source Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/41407
ISBN: 9781450314626
DOI: 10.1145/2339530.2339543
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