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EconPapers FAQ Archive maintainers FAQ Cookies at EconPapers Format for printing The RePEc blog The RePEc plagiarism page Detecting and exploiting symmetries in sequential pattern miningIkram Nekkache, Said Jabbour, Nadjet Kamel and Lakhdar SaisInternational Journal of Data Mining, Modelling and Management, 2022, vol. 14, issue 4, 309-334Abstract:In this paper, we introduce a new framework for discovering and using symmetries in sequential pattern mining tasks. Symmetries are permutations between items that leave invariant the sequential database. Symmetries present several potential benefits. They can be seen as a new kind of structural patterns expressing regularities and similarities between items. As symmetries induce a partition of the sequential patterns into equivalent classes, exploiting them would allow to improve the pattern enumeration process, while reducing the size of the output. To this end, we first address the problem of symmetry discovery from database of sequences. Then, we first show how Apriori-like algorithms can be enhanced by dynamic integration of the detected symmetries. Secondly, we provide a second symmetry breaking approach allowing to eliminate symmetries in a pre-processing step by reformulating the sequential database of transactions. Our experiments clearly show that several sequential pattern mining datasets contain such symmetry-based regularities. We also experimentally demonstrate that using such symmetries would results in significant reduction of the search space on some datasets.Keywords: data mining; sequential pattern mining; symmetries. (search for similar items in EconPapers)Date: 2022References: Add references at CitEc Citations: Track citations by RSS feedDownloads: (external link) =126663 (text/html)Access to full text is restricted to subscribers.Related works:This item may be available elsewhere in EconPapers: Search for items with the same title.Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/TextPersistent link: :ids:ijdmmm:v:14:y:2022:i:4:p:309-334Access Statistics for this articleMore articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises LtdBibliographic data for series maintained by Sarah Parker (Obfuscate( 'inderscience.com', 'informationadministrator5' )). var addthis_config = "data_track_clickback":true; var addthis_share = url:" :ids:ijdmmm:v:14:y:2022:i:4:p:309-334"Share This site is part of RePEc and all the data displayed here is part of the RePEc data set. Is your work missing from RePEc? Here is how to contribute. Questions or problems? Check the EconPapers FAQ or send mail to Obfuscate( 'oru.se', 'econpapers' ). EconPapers is hosted by the Örebro University School of Business.
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