Abstract

Automatic discovery of qualitative and quantitative patterns inherent in data sets is accomplished by use of a unified framework which employs adjusted residual analysis in statistics to test the significance of the pattern candidates generated from data sets. This framework consists of a search engine for different order patterns, a mechanism to avoid exhaustive search by eliminating impossible pattern candidates, an attributed hypergraph (AHG) based knowledge representation language and an inference engine which measures the weight of evidence of each pattern for classification and prediction. If a pattern candidate passes the statistical significance test of adjusted residual, it is regarded as a pattern and represented by an attributed hyperedge in AHG. In the task of classification and/or prediction, the weights of evidence are calculated and compared to draw the conclusion.

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