Mining Hyperclique Patterns with Confidence Pruning

Thumbnail Image

View/Download File

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


Mining Hyperclique Patterns with Confidence Pruning

Published Date






Standard association-rule mining algorithms have relied on the support-based pruning strategy to discover interesting patterns. Although this strategy can ease the bottleneck of itemset generation, it can potentially miss many interesting patterns, particularly those with low support but high confidence. The problem becomes even more critical if items have widely differing support. For such data sets, setting up support threshold too low leads to generation of too many uninteresting associations involving items withsubstantially different levels of support, and setting support threshold too high leads to elimination of all patterns involving low-support items. To address these problems, we propose the concept of a hyperclique pattern, which uses an interestingness measure called h-confidence to find patterns containing items that are highly affiliated with each other. We show that h-confidence not only possesses the desirable downward closure property for identifying highly associated patterns at low support levels, it has the ability to remove spurious associationsinvolving items from different support levels. In addition, we present an algorithm called hyperclique miner, which caneffectively prune the cross-support patterns and efficientlydiscover hyperclique patterns at all levels of support. Asdemonstrated by our extensive experiments on both real andsynthetic data sets, the performance of hyperclique miner isseveral orders of magnitude faster than frequent patterngenerating algorithms, such as Apriori and CHARM, particularly at low levels of support. Finally, we show that hyperclique patterns are very promising for clustering items in a high dimensional space.



Related to



Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Xiong, Hui; Tan, Pang-ning; Kumar, Vipin. (2003). Mining Hyperclique Patterns with Confidence Pruning. Retrieved from the University Digital Conservancy,

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.