Classification models for outbreak detection in oil and gas pollution area

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This study aim to investigate the data mining task and techniques specifically sequential pattern mining on the outbreak detection in oil and gas pollution area. The sequential pattern mining can be treated as a classification problem if enough data for certain sequence of time is available, as association problem if large number of related attributes are available, or can be seen as the deviation detection problem if the available data contain only few rare pattern or outliers. In this paper, the classification technique, decision tree is used for classification, and association rules mining is used for the outbreak detection task in oil and gas air dataset. The study found that unsupervised clustering using K-Means algorithm potentially obtain the rarely patterns of data distributing on several groups of pollutants and the average levels of supervised classification using the decision tree is a bit higher than the levels of association rules mining classification and appropriately used to classify the data by contaminants. Association rules mining on the other hand produce several sequences rules of contaminants. This study has high potential in producing quality rules for outbreak detection.

Original languageEnglish
Title of host publicationProceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 - Bandung
Duration: 17 Jul 201119 Jul 2011

Other

Other2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011
CityBandung
Period17/7/1119/7/11

Fingerprint

Pollution
Association rules
Gases
Decision trees
Impurities
Data mining
Oils
Air

Keywords

  • association and sequential patterns
  • decision tree
  • Outbreak detection

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering

Cite this

Abu Bakar, A., Idris, N., Hamdan, A. R., Othman, Z., Ahmad Nazri, M. Z., & Zainudin, S. (2011). Classification models for outbreak detection in oil and gas pollution area. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 [6021832] https://doi.org/10.1109/ICEEI.2011.6021832

Classification models for outbreak detection in oil and gas pollution area. / Abu Bakar, Azuraliza; Idris, Nurfathehah; Hamdan, Abdul Razak; Othman, Zalinda; Ahmad Nazri, Mohd Zakree; Zainudin, Suhaila.

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011. 2011. 6021832.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abu Bakar, A, Idris, N, Hamdan, AR, Othman, Z, Ahmad Nazri, MZ & Zainudin, S 2011, Classification models for outbreak detection in oil and gas pollution area. in Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011., 6021832, 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011, Bandung, 17/7/11. https://doi.org/10.1109/ICEEI.2011.6021832
Abu Bakar A, Idris N, Hamdan AR, Othman Z, Ahmad Nazri MZ, Zainudin S. Classification models for outbreak detection in oil and gas pollution area. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011. 2011. 6021832 https://doi.org/10.1109/ICEEI.2011.6021832
Abu Bakar, Azuraliza ; Idris, Nurfathehah ; Hamdan, Abdul Razak ; Othman, Zalinda ; Ahmad Nazri, Mohd Zakree ; Zainudin, Suhaila. / Classification models for outbreak detection in oil and gas pollution area. Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011. 2011.
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