document: Playing hide-and-seek with correlations We present a method for very high-dimensional correlation analysis . The method relies equally on rigorous search strategies and on human interaction . At each step , the method conservatively `` shaves off '' a fraction of the database tuples and attributes , so that most of the correlations present in the data are not affected by the decomposition . Instead , the correlations become more obvious to the user , because they are hidden in a much smaller portion of the database . This process can be repeated iteratively and interactively , until only the most important correlations remain . The main technical difficulty of the approach is figuring out how to `` shave off '' part of the database so as to preserve most correlations . We develop an algorithm for this problem that has a polynomial running time and guarantees result quality .
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