A breadth-first search algorithm for frequent itemsets based on vertical data layout

A breadth-first search algorithm for frequent itemsets based on vertical data layout | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A breadth-first search algorithm for frequent itemsets based on vertical data layout Yingchao Li, Yang Wang, Guanci Yang, Yongming Wu, Zhenqiang Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4750252/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Frequent itemset mining, a pivotal technique in the field of data mining, is aimed at identifying items that frequently co-occur in transaction databases, thereby enabling the extraction of valuable patterns and associations within large datasets. Algorithms utilizing vertical data layout typically employ depth-first search for extracting frequent itemsets. However, leveraging anti-monotonicity property to constrain the search space poses challenges. Additionally, memory consumption can be substantial, particularly when using a low minimum support threshold. In this paper, we propose integrating hashmap and list data structures to store frequent itemsets and represent the search space more efficiently. Furthermore, we introduce a breadth-first search-based approach for generating candidate itemsets while simultaneously pruning them based on anti-monotonicity principles. Support counting is performed using bitwise operators within the concept and technique of vertical database representation. Building upon two formats of vertical databases (Tidset and Diffset), we present two novel algorithms: Tidset-BFS and Diffset-BFS respectively. To evaluate their performance, extensive experiments are conducted, comparing them with a prominent negFIN algorithm across various real-world and synthetic datasets. The experimental results demonstrate that our proposed algorithms exhibit significantly improved efficiency across most datasets. Data mining Frequent itemsets Vertical data layout Pruning candidates Compressed bitmaps Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4750252","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331606074,"identity":"65d10b1b-c3dd-412c-84d9-ad43815a4dd8","order_by":0,"name":"Yingchao Li","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Yingchao","middleName":"","lastName":"Li","suffix":""},{"id":331606075,"identity":"5f843891-c9a5-4d3d-89fa-d4312a207b59","order_by":1,"name":"Yang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDADfgjFTIIWyQaStRgcIFaLwfGzh1/z1Nyx23x++TMJhgrrxAb2swfwazmTl2bNc+xZ8rYbD9IkGM6kJzbw5CUQcE+OmXEO2+FksxsHjkkwth1ObJDgMcCv5fwboJZ/h5ONZxxsk2D8R4yWGznGj3PbDtsZ8DezSTA2EKFF8sYbM+a/fYcTJG6wMVskHEs3buPJwa+F73yO8ccZ3w7b8/cff3jjQ421bD/7GfxaFA4wsEkAaaB7EhgYgIiBDa96IJBvYGD+AKTtGfgPEFI7CkbBKBgFIxUAAMivSxq2I+DZAAAAAElFTkSuQmCC","orcid":"","institution":"Guizhou University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":331606077,"identity":"a2a58ef1-fa9f-432c-ba8b-051ec602ecee","order_by":2,"name":"Guanci Yang","email":"","orcid":"","institution":"Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Guanci","middleName":"","lastName":"Yang","suffix":""},{"id":331606080,"identity":"ecf12d9f-f3df-4fbe-8059-8524a49506a7","order_by":3,"name":"Yongming Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yongming","middleName":"","lastName":"Wu","suffix":""},{"id":331606084,"identity":"df665e52-1056-4873-aba7-dcc8947149e5","order_by":4,"name":"Zhenqiang Xie","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhenqiang","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-07-16 13:59:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4750252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4750252/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66500590,"identity":"c152843e-e267-486d-8f99-a01fabf56b9d","added_by":"auto","created_at":"2024-10-13 14:46:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":775319,"visible":true,"origin":"","legend":"","description":"","filename":"Abreadthfirstsearchalgorithmforfrequentitemsetsbasedonverticaldatalayout.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4750252/v1_covered_d16a7e7f-3489-4b8e-835c-aef767ec853a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A breadth-first search algorithm for frequent itemsets based on vertical data layout","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Data mining, Frequent itemsets, Vertical data layout, Pruning candidates, Compressed bitmaps","lastPublishedDoi":"10.21203/rs.3.rs-4750252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4750252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Frequent itemset mining, a pivotal technique in the field of data mining, is aimed at identifying items that frequently co-occur in transaction databases, thereby enabling the extraction of valuable patterns and associations within large datasets. Algorithms utilizing vertical data layout typically employ depth-first search for extracting frequent itemsets. However, leveraging anti-monotonicity property to constrain the search space poses challenges. Additionally, memory consumption can be substantial, particularly when using a low minimum support threshold. In this paper, we propose integrating hashmap and list data structures to store frequent itemsets and represent the search space more efficiently. Furthermore, we introduce a breadth-first search-based approach for generating candidate itemsets while simultaneously pruning them based on anti-monotonicity principles. Support counting is performed using bitwise operators within the concept and technique of vertical database representation. Building upon two formats of vertical databases (Tidset and Diffset), we present two novel algorithms: Tidset-BFS and Diffset-BFS respectively. 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