Abstract
Mining frequent patterns on the vertical data structures usually shows improvements of performance over the classical horizontal structure. This is because the vertical data structure supports fast frequency counting via intersection operations on transaction identifiers (TIDs). Recently, Diffsets by M.J. Zaki and K. Gouda (2001), a vertical data representation, has been introduced for the sake of the size of memory required to store intermediate TIDs in the mining process. In this paper, we present a new vertical mining algorithm on the Diffset structure called Fast Diffsets Vertical Mining (FDVM). Primarily, FDVM uses the concept of pattern growth on the Diffset structure, and we show that FDVM outperforms previous methods in mining the complete set of frequent patterns. Our experimental results indicate that significant performance improvement can be gained, especially for large databases, over previously proposed vertical and horizontal mining algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
