Machine Learning by Data Mining REPTree and M5P for Predicating Novel Information for PM10

Abstract

We examined data mining as a technique to extract knowledge from database to predicate PM₁₀ concentration related to meteorological parameters. The purpose of this paper is to compare between the two types of machine learning by data mining decision tree algorithms Reduced Error Pruning Tree (REPTree) and divide and conquer M5P to predicate Particular Matter 10 (PM₁₀) concentration depending on meteorological parameters. The results of the analysis showed M5P tree gave higher correlation compared with REPTree, moreover lower errors, and higher number of rules, the elapsed time for processing REPTree is less than the time processing of M5P. Both of these trees proved that humidity absorbed PM₁₀. The paper recommends REPTree and M5P for predicting PM₁₀ and other pollution gases.