Computational method for discovering patterns in data sets

Details Computational method for discovering patterns in data sets Computational method for discovering patterns in data sets

G - Physics

06

F

G06F 17/30 (2006.01)

Patent

CA 2188248

Automatic discovery of qualitative and quantitative patterns inherent in data 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 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 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.

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