Mining convergent and divergent sequences in multidimensional data
by M. Plantevit, A. Laurent, M. Teisseire
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 4, No. 3/4, 2009

Abstract: Online Analytical Processing (OLAP) mining provides useful knowledge to decision makers from multidimensional data stored in data warehouses. However, it is still difficult to find data mining tools taking all the data specificities (e.g., multidimensionality, hierarchies, time) into account. In this paper, we propose an original method to discover multidimensional sequential patterns among several levels of hierarchies. We define two types of multidimensional sequences: convergent sequences, where the elements become more and more precise, and divergent sequences where the elements become more and more general. A pattern-growth based algorithm is proposed and is shown to be efficient in our experiments.

Online publication date: Tue, 03-Nov-2009

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