Efficient Closed Pattern Mining in the Presence of Tough Block Constraints

No Thumbnail Available

View/Download File

Persistent link to this item

View Statistics

Journal Title

Journal ISSN

Volume Title


Efficient Closed Pattern Mining in the Presence of Tough Block Constraints

Published Date






In recent years, various constrained frequent pattern mining problem formulations and associated algorithms have been developed that enable the user to specify various itemset-based constraints that better capture the underlying application requirements and characteristics. In this paper we introduce a new class of {em block} constraints that determine the significance of an itemset pattern by considering the dense block that is formed by the pattern's items and its associated set of transactions. Block constraints provide a natural framework by which a number of important problems can be specified and make it possible to solve numerous problems on binary and real-valued datasets. However, developing computationally efficient algorithms to find these block constraints poses a number of challenges as unlike the different itemset-based constraints studied earlier, these block constraints are {em tough} as they are neither anti-monotone, monotone, nor convertible. To overcome this problem, we introduce a new class of pruning methods that can be used to significantly reduce the overall search space and make it possible to develop computationally efficient block constraint mining algorithms. We present an algorithm called cbminer that takes advantage of these pruning methods to develop an algorithm for finding the closed itemsets that satisfy the block constraints. Our extensive performance study shows that cbminer generates more concise result set and can be order(s) of magnitude faster than the traditional frequent closed itemset mining algorithms.



Related to



Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Gade, Krishna; Wang, Jianyong; Karypis, George. (2003). Efficient Closed Pattern Mining in the Presence of Tough Block Constraints. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215588.

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.