Abstract
A data mining method finds hidden patterns in massive datasets for study. It is commonly used in high-tech fields such as image processing and artificial intelligence, due to its ability to compute data statistics and pattern processing problems efficiently. This study investigates data mining in multiobjective dynamic software development based on dynamic traffic congestion prediction. Since traffic data can fluctuate at any time, it is typically challenging to develop more accurate mathematical and theoretical models. We integrate data mining techniques into the software for predicting traffic congestion and develop a new algorithm for discriminating traffic congestion. Using a combination of the 3 criteria and the SVM algorithm, along with massive amounts of data, our prediction accuracy is significantly enhanced.
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