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An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia

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@article{IJASEIT1482,
   author = {Mahmoud Sammour and Zulaiha Othman},
   title = {An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {1127--1133},
   keywords = {Agglomerative Hierarchical Clustering; Dynamic Time Warping; Ozone Analysis; Time Series.},
   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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1482},
   doi = {10.18517/ijaseit.6.6.1482}
}

EndNote

%A Sammour, Mahmoud
%A Othman, Zulaiha
%D 2016
%T An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia
%B 2016
%9 Agglomerative Hierarchical Clustering; Dynamic Time Warping; Ozone Analysis; Time Series.
%! An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia
%K Agglomerative Hierarchical Clustering; Dynamic Time Warping; Ozone Analysis; Time Series.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1482
%R doi:10.18517/ijaseit.6.6.1482
%J International Journal on Advanced Science, Engineering and Information Technology
%V 6
%N 6
%@ 2088-5334

IEEE

Mahmoud Sammour and Zulaiha Othman,"An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1127-1133, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1482.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sammour, Mahmoud
AU  - Othman, Zulaiha
PY  - 2016
TI  - An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 1127
EP  - 1133
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Agglomerative Hierarchical Clustering; Dynamic Time Warping; Ozone Analysis; Time Series.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1482
DO  - 10.18517/ijaseit.6.6.1482

RefWorks

RT Journal Article
ID 1482
A1 Sammour, Mahmoud
A1 Othman, Zulaiha
T1 An Agglomerative Hierarchical Clustering with Various Distance Measurements for Ground Level Ozone Clustering in Putrajaya, Malaysia
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 1127
OP 1133
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Agglomerative Hierarchical Clustering; Dynamic Time Warping; Ozone Analysis; Time Series.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1482
DO  - 10.18517/ijaseit.6.6.1482