COMPUTATIONAL METHOD FOR DISCOVERING PATTERNS IN DATA SETS

摘要

Automatic discovery of qualitative and quantitative patterns inherent in data se ts is accomplished by use of a unified framework which employs adjusted residual analy sis in statistics to test the significance of the pattern candidates generated from dat a 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 hy pergraph (AHG) based knowledge representation language and an inference engine which meas ures the weight of evidence of each pattern for classification and prediction. If a p attern candidate passes the statistical significance test of adjusted residual, it is regard ed 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 conc lusion.