Inter Transactional Association Rule Mining using Boolean Matrix
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2012, Vol 1, Issue 2
Abstract
One of the extensions of the data mining is Temporal data mining, which mines or discovers knowledge and patterns from temporal information which includes time attribute analysis. From the various types of temporal association rule mining, much of the literature focuses on intra transaction association rules, which deal with relations within transactions. Only a few algorithms exist to mine inter transaction association rules, where relations across transactions are analyzed. With the help of inter transactional association rules, one can achieve the benefits to know that the item B follows the item A, but also get an indication on when this is supposed to happen. This paper is presenting preliminary framework to find interesting temporal inter transactional associated patterns with the help of modification in data preparation step and with the help of Boolean matrix to process the data. During the data preparation step, the transaction data are converted to Mega transactions. We apply this step after the generation of 1-frequent items, so that it helps to reduce the number of items in the Mega transactions. And then, completing remaining part of frequent item analysis to discover the temporal relationships from data. As the Boolean matrix is generated from the Mega transactions no more dataset scanning is required and thus minimizes the overall execution time.
Authors and Affiliations
Dipti Rana, N. J. Mistry, M. M. Raghuwanshi
Keywords
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