Comparison of Two New Data Mining Approach with Existing Approaches

Jun Zhang,Junjun Liu,Qing E Wu

doi:10.19026/rjaset.11.2143

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.

Highlights

Because of the rapid increase of measurement data in engineering application and the participation of human, the uncertainty of information in data is more prominent and the relationship among data is more complex
This study introduces two new approaches on data mining, uses them and other classical supervised learning data mining technologies to learn and classify 1080 data, validates the feasibility and effectiveness for the new data mining approach and compares the performance of these approaches with each other, so as to hope that can select a best mining approach for fault diagnosis in software system
The decision tree, support vector machine, logistic regression, statistical approach and neural networks are followed in turn

Summary

Introduction

Because of the rapid increase of measurement data in engineering application and the participation of human, the uncertainty of information in data is more prominent and the relationship among data is more complex. Experiment and comparison: Chen et al (2008), Aburrous et al (2010) and Khalifelu and Gharehchopogh (2012) gave the forecast accuracy of the decision tree approach was higher than the corresponding value of other approaches and its standard deviation was less than that of other approaches.

Results

Conclusion