Abstract

This study studies two uncertainty data mining approaches and gives the two algorithms implementation in the software system fault diagnosis. We discuss the application comparison of the two data mining approaches with four classical data mining approaches in software system fault diagnosis. We measure the performance of each approach from the sensitivity, specificity, accuracy rate and run-time and choose an optimum approach from several approaches to do comparative study. On the data of 1080 samples, the test results show that the sensitivity of the fuzzy incomplete approach is or so 95.0%, the specificity is or so 94.32%, the accuracy is or so 94.54%, the run-time is 0.41 sec. Synthesizing all the performance measures, the performance of the fuzzy incomplete approach is best, followed by decision tree and support vector machine is better and then followed by Logistic regression, statistical approach and the neural networks in turn. These researches in this study offer a new thinking approach and a suitable choice on data mining.

Keywords:

Data mining approach, fuzzy incompleteness, performance indexes, statistical approach,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.