Detecting Spatial Outliers using Bipartite Outlier Detection Methods
Outlier detection, as a data mining task, is to identify a small set of data that is considerably dissimilar or inconsistent with the remainder of the data. Spatial outliers are spatially referenced objects whose non-spatial attribute values are significantly different from that of their neighbors. Identification of spatial outliers can lead to the discovery of unexpected, interesting spatial patterns for further investigation. In this paper, bipartite methods are used to detect spatial outliers based on the concepts of spatial point estimation and spatial statistical theory. Two point estimation methods are introduced to estimate values of spatial points. The concept of Z-score is used to evaluate the deviation of ratios of estimated values vs. true values from average ratio in the study space. Two algorithms are proposed to identify spatial outliers using different methods. These algorithms are used in New Mexico Produced Water Chemistry Database (PWCD). Results show that outlier detection can aid in bad data checking and the analysis of produced water (in oil and gas production) related problems.
M. Wei et al., "Detecting Spatial Outliers using Bipartite Outlier Detection Methods," Proceedings of the International Conference on Information and Knowledge Engineering (2004, Las Vegas, NV), pp. 236-242, Unknown, Jun 2004.
International Conference on Information and Knowledge Engineering (2004: Jun. 21-24, Las Vegas, NV)
Geosciences and Geological and Petroleum Engineering
Keywords and Phrases
Bipartite Spatial Outlier Detection; Knowledge Discovery; Point Estimation; Spatial Data Mining; Z-Scores; Algorithms; Data Reduction; Estimation; Information Analysis; Relational Database Systems; Statistical Tests
International Standard Book Number (ISBN)
Article - Conference proceedings
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