Abstract
Mining frequent itemsets (FIs) has been developing in recent years. However, little attention has been paid to efficient methods for mining in multidimensional databases. In this paper, we propose a new method with a supporting structure called AIO-tree (Attributes Itemset Object identifications – tree) for mining FIs from multidimensional databases. This method need not transform the database into the transaction database, and it is based on the intersections of object identifications for fast computing the support of itemsets. We compare our method to dEclat (after transformation to a transaction database) and indeed claim that they are faster than dEclat.
This work was supported by Vietnam’s National Foundation for Science and Technology Development (NAFOSTED), project ID: 102.01-2010.02.
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Vo, B., Le, B., Nguyen, T.N. (2011). Mining Frequent Itemsets from Multidimensional Databases. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6591. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20039-7_18
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DOI: https://doi.org/10.1007/978-3-642-20039-7_18
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