| Autor: |
Suris, Fatin Nur Afiqah, Bakar, Mohd Aftar Abu, Ariff, Noratiqah Mohd, Nadzir, Mohd Shahrul Mohd |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3150 Issue 1, p1-9, 9p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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