Association pattern of NO2 and NMHC towards high ozone concentration in klang

Association rules techniques has advantages to discover knowledge such as frequent pattern or infrequent patterns. Therefore, this paper present the discovering of association rules based on data mining technique towards climatological time series data. For this purpose, k-mean clustering technique has been proposed to determine the interval for all variables separately and use Apriori algorithm to find association rule between the variables. The aims of this study is to determine the useful patterns on the high and low concentration of Ozone surrounding Klang areas. The experiment was conducted using hourly ozone dataset collected at Klang station in years 1997 until 2012. The result has found 17 rules which contribute in identifying the behaviour and the pattern of the variables.