COMPUTATIONAL METHOD FOR DISCOVERING PATTERNS IN DATA SETS

摘要

Automatic discovery of qualitative and quantitative patterns inherent in dat a sets is accomplished by use of a muffed 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 attribute d 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 candi- date passes the statistical significance test of adjusted residual, it is regarded as a pattern and represented by an attributed hyper edge in AHG. In the task of classification and/or prediction, the weights of evidence are calculated and compared to draw the conclusion.