Abstract
We present a survey of data exploration methods that extract multidimensional patterns from datasets consisting of dimension and measure attributes. These patterns are designed to summarize common properties of tuples sharing the same values of the measure attributes. We review motivating applications, we provide a categorization of the characteristics of patterns produced by various solutions to this problem, we categorize and experimentally evaluate commonly used performance optimizations, and we suggest directions for future research.
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