Chester, Sean2009-04-282009-04-2820092009-04-28http://hdl.handle.net/1828/1370Frequent itemset mining, the task of finding sets of items that frequently occur to- gether in a dataset, has been at the core of the field of data mining for the past sixteen years. In that time, the size of datasets has grown much faster than has the ability of existing algorithms to handle those datasets. Consequentely, improvements are needed. In this thesis, we take the classic algorithm for the problem, A Priori, and improve it quite significantly by introducing what we call a vertical sort. We then use the benchmark large dataset, webdocs, from the FIMI 2004 conference to contrast our performance against several state-of-the-art implementations and demonstrate not only equal efficiency with lower memory usage at all support thresholds, but also the ability to mine support thresholds as yet unattempted in literature. We also indicate how we believe this work can be extended to achieve yet more impressive results.enAvailable to the World Wide Webdata miningapriorifrequent itemset miningmachine learningUVic Subject Index::Sciences and Engineering::Applied Sciences::Computer scienceThesis