An agglomerative hierarchical clustering with various distance measurements for ground level ozone clustering in Putrajaya, Malaysia

Mahmoud Sammour, Zulaiha Ali Othman

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Ground level ozone is one of the common pollution issues that has a negative influence on human health. The key characteristic behind ozone level analysis lies on the complex representation of such data which can be shown by time series. Clustering is one of the common techniques that have been used for time series metrological and environmental data. The way that clustering technique groups the similar sequences relies on a distance or similarity criteria. Several distance measures have been integrated with various types of clustering techniques. However, identifying an appropriate distance measure for a particular field is a challenging task. Since the hierarchical clustering has been considered as the state of the art for metrological and climate change data, this paper proposes an agglomerative hierarchical clustering for ozone level analysis in Putrajaya, Malaysia using three distance measures i.e. Euclidean, Minkowski and Dynamic Time Warping. Results shows that Dynamic Time Warping has outperformed the other two distance measures.

Original languageEnglish
Pages (from-to)1127-1133
Number of pages7
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume6
Issue number6
DOIs
Publication statusPublished - 2016

Fingerprint

Distance measurement
Ozone
Malaysia
ozone
Cluster Analysis
Time series
time series analysis
Climate change
human health
Pollution
pollution
methodology
Health
climate change
Climate Change

Keywords

  • Agglomerative hierarchical clustering
  • Dynamic time warping
  • Ozone analysis
  • Time series

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Computer Science(all)
  • Engineering(all)

Cite this

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abstract = "Ground level ozone is one of the common pollution issues that has a negative influence on human health. The key characteristic behind ozone level analysis lies on the complex representation of such data which can be shown by time series. Clustering is one of the common techniques that have been used for time series metrological and environmental data. The way that clustering technique groups the similar sequences relies on a distance or similarity criteria. Several distance measures have been integrated with various types of clustering techniques. However, identifying an appropriate distance measure for a particular field is a challenging task. Since the hierarchical clustering has been considered as the state of the art for metrological and climate change data, this paper proposes an agglomerative hierarchical clustering for ozone level analysis in Putrajaya, Malaysia using three distance measures i.e. Euclidean, Minkowski and Dynamic Time Warping. Results shows that Dynamic Time Warping has outperformed the other two distance measures.",
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