Abstract
Exploratory data analysis is a widely used technique to determine which factors have the most influence on data values in a multi-way table, or which cells in the table can be considered anomalous with respect to the other cells. In particular, median polish is a simple yet robust method to perform exploratory data analysis. Median polish is resistant to holes in the table (cells that have no values), but it may require many iterations through the data. This factor makes it difficult to apply median polish to large multidimensional tables, since the I/O requirements may be prohibitive. This paper describes a technique that uses median polish over an approximation of a datacube, easing the burden of I/O. The cube approximation is achieved by fitting log-linear models to the data. The results obtained are tested for quality, using a variety of measures. The technique scales to large datacubes and proves to give a good approximation of the results that would have been obtained by median polish in the original data.
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