NDLI: Classification models for outbreak detection in oil and gas pollution area

Content Provider	IEEE Xplore Digital Library
Author	Bakar, A.A. Idris, N. Hamdan, A.R. Othman, Z. Nazari, M.Z.A. Zainudin, S.
Copyright Year	2011
Description	Author affiliation: Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan, Malaysia, 43600 Bangi Selangor Darul Ehsan, MALAYSIA (Bakar, A.A.; Idris, N.; Hamdan, A.R.; Othman, Z.; Nazari, M.Z.A.; Zainudin, S.)
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.
Starting Page	1
Ending Page	6
File Size	767457
Page Count	6
File Format	PDF
ISBN	9781457707537
ISSN	21556830
e-ISBN	9781457707520
DOI	10.1109/ICEEI.2011.6021832
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-17
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy Pollution decision tree Outbreak detection association and sequential patterns Classification algorithms Association rules Diseases
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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