International Journal on Advanced Science, Engineering and Information Technology, Vol. 8 (2018) No. 6, pages: 2683-2689, DOI:10.18517/ijaseit.8.6.6689

Rules Discovery of High Ozone in Klang Areas using Data Mining Approach

Abstract

Ground level ozone (O₃) is one of the common pollution issues that has a negative influence on human health. However, the increasing trends in O₃ level nowadays which due to rapid development has become a great concern over the world. Thus, developing an accurate O₃ forecasting model is necessary. However, the interesting pattern from the data should be identified beforehand. Association rules is a data mining technique that has an advantage to discover frequent patterns in a dataset, which subsequently will be useful in the research domain. Therefore, this paper presents the discovering knowledge based on association rules and clustering technique towards a climatological O₃ dataset. In this study, the data was analysed to find the behaviour of each precursors. Later K-means clustering technique was used to find the suitable range for each chosen variable independently, then applied Apriori based association rules technique to present the behaviours in a meaningful and understandable format. The climatological O₃ time series data has been collected from Department of Environment for Klang station from year 1997 to 2012. However, the proposed method only applied on high O₃ concentration data during stated years to find the association pattern. The outcome has discovered 17 strong rules.  The patterns and behaviours of the selected variables during high O₃ concentration has been discovered. The rules are benefit to the government on how to control the air quality later.

Keywords:

data mining; ozone; association rule; apriori; clustering.

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