Abstract
Existing algorithms for mining association patterns often rely on the support-based pruning strategy to prune a combinatorial search space. However, this strategy is not effective for discovering potentially interesting patterns at low levels of support. Also, it tends to generate too many spurious patterns involving items which are from different support levels and are poorly correlated. In this paper, we present a framework for mining highly-correlated association patterns called hyperclique patterns. In this framework, an objective measure called h-confidence is applied to discover hyperclique patterns. We prove that the items in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by the cosine similarity (uncentered Pearson's correlation coefficient). Also, we show that the h-confidence measure satisfies a cross-support property which can help efficiently eliminate spurious patterns involving items with substantially different support levels. Indeed, this cross-support property is not limited to h-confidence and can be generalized to some other association measures. In addition, an algorithm called hyperclique miner is proposed to exploit both cross-support and anti-monotone properties of the h-confidence measure for the efficient discovery of hyperclique patterns. Finally, our experimental results show that hyperclique miner can efficiently identify hyperclique patterns, even at extremely low levels of support.
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It is available at http://www.almaden.ibm.com/ software/quest/resources.
This is observed on Sun Ultra 10 work station with a 440 MHz CPU and 128 Mbytes of memory.
When computing Pearson's correlation coefficient, the data mean is not subtracted.
Note that the number of items shown in Table 4 for pumsb, pumsb * are somewhat different from the numbers reported in Zaki and Hsiao (2002), because we only consider item IDs for which the count is at least one. For example, although the minimum item ID in pumsb is 0 and the maximum item ID is 7116, there are only 2113 distinct item IDs that appear in the data set.
The data set is available at http://trec.nist.gov.
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Acknowledgments
This work was partially supported by NSF grant # IIS-0308264, DOE/LLNL W-7045-ENG-48, and by Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement number DAAD19-01-2-0014. The content of this work does not necessarily reflect the position or policy of the government and no official endorsement should be inferred. Access to computing facilities was provided by the AHPCRC and the Minnesota Supercomputing Institute. Finally, we would like to thank Dr. Mohammed J. Zaki for providing us the CHARM code. Also, we would like to thank Dr. Shashi Shekhar, Dr. Ke Wang, and Michael Steinbach for valuable comments.
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Xiong, H., Tan, PN. & Kumar, V. Hyperclique pattern discovery. Data Min Knowl Disc 13, 219–242 (2006). https://doi.org/10.1007/s10618-006-0043-9
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DOI: https://doi.org/10.1007/s10618-006-0043-9