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Exceptional object analysis for finding rare environmental events from water quality datasets

He, Jing, Zhang, Yanchun and Huang, Guangyan 2012, Exceptional object analysis for finding rare environmental events from water quality datasets, Neurocomputing, vol. 92, pp. 69-77, doi: 10.1016/j.neucom.2011.08.036.

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Title Exceptional object analysis for finding rare environmental events from water quality datasets
Author(s) He, Jing
Zhang, Yanchun
Huang, Guangyan
Journal name Neurocomputing
Volume number 92
Start page 69
End page 77
Total pages 9
Publisher Elsvier
Place of publication Amsterdam, The Netherlands
Publication date 2012-09-01
ISSN 0925-2312
1872-8286
Summary This paper provides a novel Exceptional Object Analysis for Finding Rare Environmental Events (EOAFREE). The major contribution of our EOAFREE method is that it proposes a general Improved Exceptional Object Analysis based on Noises (IEOAN) algorithm to efficiently detect and rank exceptional objects. Our IEOAN algorithm is more general than already known outlier detection algorithms to find exceptional objects that may be not on the border; and experimental study shows that our IEOAN algorithm is far more efficient than directly recursively using already known clustering algorithms that may not force every data instance to belong to a cluster to detect rare events. Another contribution is that it provides an approach to preprocess heterogeneous real world data through exploring domain knowledge, based on which it defines changes instead of the water data value itself as the input of the IEOAN algorithm to remove the geographical differences between any two sites and the temporal differences between any two years. The effectiveness of our EOAFREE method is demonstrated by a real world application - that is, to detect water pollution events from the water quality datasets of 93 sites distributed in 10 river basins in Victoria, Australia between 1975 and 2010. © 2012 Elsevier B.V..
Language eng
DOI 10.1016/j.neucom.2011.08.036
Field of Research 08 Information And Computing Sciences
09 Engineering
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2012, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083655

Document type: Journal Article
Collection: School of Information Technology
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