Rules Discovery of High Ozone in Klang Areas using Data Mining Approach

Zulaiha Ali Othman, Noraini Ismail, Azuraliza Abu Bakar, Mohd Talib Latif, Sharifah Mastura Syed Abdullah

Abstract


Ground level ozone (O3) is one of the common pollution issues that has a negative influence on human health. However, the increasing trends in O3 level nowadays which due to rapid development has become a great concern over the world. Thus, developing an accurate O3 forecasting model is necessary. However, the interesting pattern from the data should be identified beforehand. Association rules is a data mining technique that has an advantage to discover frequent patterns in a dataset, which subsequently will be useful in the research domain. Therefore, this paper presents the discovering knowledge based on association rules and clustering technique towards a climatological O3 dataset. In this study, the data was analysed to find the behaviour of each precursors. Later K-means clustering technique was used to find the suitable range for each chosen variable independently, then applied Apriori based association rules technique to present the behaviours in a meaningful and understandable format. The climatological O3 time series data has been collected from Department of Environment for Klang station from year 1997 to 2012. However, the proposed method only applied on high O3 concentration data during stated years to find the association pattern. The outcome has discovered 17 strong rules.  The patterns and behaviours of the selected variables during high O3 concentration has been discovered. The rules are benefit to the government on how to control the air quality later.


Keywords


data mining; ozone; association rule; apriori; clustering.

Full Text:

PDF

References


R. Atkinson et al., “Long-term exposure to ambient ozone and mortality: A quantitative systematic review and meta-analysis of evidence from cohort studies,†in Epidemiology, 2016, vol. 6.

Rozalina Chuturkova, “Ozone and ozone precursors in urban atmosphere,†Journal scientific and applied research, 2015, vol. 8, pp. 31-39.

C. Gavrila, A. Coman, I. Gruia, F. Ardelean, and A. Vartires, “Prediction method applied for the evaluation of the tropospheric ozone concentrations in Bucharest,†Rom. Journ. Phys., Bucharest, 2016, vol. 61, pp. 1067–1078.

Fatimah Ahmad, Mohd Talib Latif, Rosy Tang, Liew Juneng, Doreena Dominick, and Hafizan Juahir, “Variation of surface ozone exceedance around Klang Valley, Malaysia,†Atmospheric Research, 2014, vol. 139, 116-127.

Mahmoud Sammour and Zulaiha Ali 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, 2016, vol. 6, pp. 1127-1133.

D. Adhikary, and S. Roy, “Mining quantitative association rules in real-world databases: A review,†1st International Conference on Computing and Communication Systems, 2015, vol. 1, pp. 87-92.

J. Caiado, E.A. Maharaj, and P. D’urso, Time series clustering. Handbook of cluster analysis. Chapman and Hall/CRC, Boca Raton, Florida Google Scholar. 2015.

S. Aghabozorgi, A.S. Shirkhorshidi and T.Y. Wah, “Time-series clustering: A decade review,†Information Systems, 2015, vol. 53, pp.16-38.

M. Martínez-Ballesteros, F. Martínez-Ãlvarez, A. Troncoso, and J. C. Riquelme, Quantitative association rules applied to climatological time series forecasting, Springer-Verlag Berlin Heidelberg, 2009 pp. 284–291.

C. Tew, C. Giraud-Carrier, K. Tanner and S. Burton, “Behavior-based clustering and analysis of interestingness measures for association rule mining,†Data Mining and Knowledge Discovery, 2014, vo. 28, pp. 1004-1045.

K. N. Jallad, and Cynthia Espada-Jallad, “Analysis of ambient ozone and precursor monitoring data in a densely populated residential area of Kuwait,†Journal of Saudi Chemical Society, 2010, vol. 14, pp. 363–372.

Ghassan Saleh Al-Dharhani, Zulaiha Ali Othman, Azuraliza Abu Bakar, and Sharifah Mastura Syed Abdullah, "Fuzzy-based shapelets for mining climate change time series patterns," Advances in Visual Informatics, Lecture Notes in Computer Science, 2015, vol. 9423, pp. 38-50.

World Health Organisation. Review of evidence on health aspects of air pollution - REVIHAAP project: final technical report. 2013 [10 April 2017].

T. Tassa, “Secure mining of association rules in horizontally distributed databases,†IEEE Transactions on Knowledge and Data Engineering, 2014, vol. 26(4), pp. 970-983.

M. Kantardzic, “Data mining: concepts, models, methods, and algorithms,†John Wiley & Sons, 2011.

J. Dongre, G. L. Prajapati, and S. V. Tokekar, “The role of Apriori algorithm for finding the association rules in Data mining,†IEEE International conference In Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014, pp. 657-660.

S. Qin, F. Liu, C. Wang, Y. Song, and J. Qu. “Spatial-temporal analysis and projection of extreme particulate matter (PM10 and PM2. 5) levels using association rules: A case study of the Jing-Jin-Ji region, China,†Atmospheric Environment, 2015, vol. 120, 339-350.

[

J. R. Horne, “Impact of global climate change on ozone, particulate, and secondary organic aerosol concentrations in California: a model perturbation analysis,†University of California, Irvine, 2015.

M. Martínez-Ballesteros, A. Troncoso, F. Martínez-Ãlvarez, and J. C. Riquelme, “Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution,†Integrated Computer-Aided Engineering, 2010, vol. 17 (3), 227-242.

M. Martínez-Ballesteros, S. Salcedo-Sanz, J. C. Riquelme, C. Casanova-Mateo, and J. L. Camacho, “Evolutionary association rules for total ozone content modeling from satellite observations,†Chemometrics and Intelligent Laboratory Systems, 2011, vol. 109 (2), 217-227.

S. N. Matsunaga, S. Chatani, T. Morikawa, S. Nakatsuka, J. Suthawaree, Y. Tajima, and H. Minoura, “Evaluation of non-methane hydrocarbon (NMHC) emissions based on an ambient air measurement in the Tokyo area, Japan,†Atmospheric Environment, 2010, vol. 44 (38), 4982-4993.




DOI: http://dx.doi.org/10.18517/ijaseit.8.6.6689

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development