Abstract: A support subset significant algorithm based on equivalence relation (S3ER) is presented to compute the significance of the quality characteristic. In S3ER, first, the support of conditional attribution value to the decision attribution is defined. Then, the discrimination power of the conditional attribution value is defined. Finally, the significant of conditional attribute to the decision attribute can be got by calculating the average value of the discrimination power of the conditional attribution value. At the same time, the S3ER can predict the type of data from test dataset and compute the support subset of the decision attribute. Some decision rules can be extracted from the support subset. In the experiment, the S3ER, Weighted KNN (Weighted K-nearest neighbor) and C4.5 compared their classification accuracy on five datasets from UCI repository. The S3ER is used in the manufacturing process of a Chinese aviation corporation to compute the significant, predict the test data and extract the rules for the support subset, so the quality of product of the aviation corporation can be improved.

Key words: quality improvement;attribution significant;support subset;equivalence relation