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Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns. The identified clusters were analyzed to gain meaningful information regarding air quality patterns in Malaysia and to identify characterization for each cluster. PM10 time series data from 5 July 2017 to 31 January 2019, obtained from the Malaysian Department of Environment and Dynamic Time Warping as the dissimilarity measure were used in this study. At the same time, k-Means, Partitioning Around Medoid, agglomerative hierarchical clustering, and Fuzzy k-Means were the algorithms used for clustering. The results portray that the categories and activities of locations of the monitoring stations do not directly influence the pattern of the PM10 values, instead, the clusters formed are mainly influenced by the region and geographical area of the locations.

Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping