Clustering of air monitoring stations in Malaysia based on PM10 time series data by using K-shape

Time series clustering is a data mining technique where it involves grouping of time series data into a few separate clusters. In this paper, a shape-based clustering is applied on time series of PM10 data collected from 60 air quality monitoring stations in Malaysia from 5th July 2017 to 31st December 2018 obtained from Malaysian Department of Environment. k-shape clustering method along with shape-based distance (SBD) were used to cluster the PM10 time series data. Air quality patterns were analysed from the clusters formed. The results show that the clusters formed using the method used are very well separated and mainly influenced by the region and geographical locations instead of the station’s categories or man activities at the locations.