Navigating Multidimensional Data Using Sk-Association Rules
نویسندگان
چکیده
Navigating through multidimensional data cubes is a non-trivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data in different perspectives through roll-up, drill-down, and slicing-dicing, it offers only minimal guidance to end users in the actual knowledge discovery process. It is impractical to navigate through the enormous numbers of cuboids that usually make up data cubes, and consequently users are likely to miss out valuable information in their cube navigation. In this paper, we address this problem by proposing a DIscovery of Sk-Association Rules (DISAR) algorithm to drive the knowledge discovery process. First, we develop a new set of rules known as skassociation rules using a powerful test of skewness on the pairs of lattice nodes. Second, we capture the navigation paths in the data cubes by using sk-association rules. Third, we use the sk-association rules to enhance the navigation capabilities in the data cubes. Experimental results demonstrate detailed evaluation of sk-association rules.
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