Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted DatasetsReportar como inadecuado


Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets


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1

Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

2

Key Laboratory of the Earth Observation, Beijing 100094, China

3

Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China





*

Author to whom correspondence should be addressed.



Academic Editor: Wolfgang Kainz

Abstract The ENSO El Niño Southern Oscillation is the dominant inter-annual climate signal on Earth, and its relationships with marine environments constitute a complex interrelated system. As traditional methods face great challenges in analyzing which, how and where marine parameters change when ENSO events occur, we propose an ENSO-oriented marine spatial association pattern EOMSAP mining algorithm for dealing with multiple long-term raster-formatted datasets. EOMSAP consists of four key steps. The first quantifies the abnormal variations of marine parameters into three levels using the mean-standard deviation criteria of time series; the second categorizes La Niña events, neutral conditions, or El Niño events using an ENSO index; then, the EOMSAP designs a linking–pruning–generating recursive loop to generate m + 1-candidate association patterns from an m-dimensional one by combining a user-specified support with a conditional support; and the fourth generates strong association patterns according to the user-specified evaluation indicators. To demonstrate the feasibility and efficiency of EOMSAP, we present two case studies with real remote sensing datasets from January 1998 to December 2012: one considers performance analysis relative to the ENSO-Apriori and Apriori methods; and the other identifies marine spatial association patterns within the Pacific Ocean. View Full-Text

Keywords: data mining; marine spatial association pattern; marine remote sensing products; ENSO; Pacific Ocean data mining; marine spatial association pattern; marine remote sensing products; ENSO; Pacific Ocean





Autor: Xue Cunjin 1,2 and Liao Xiaohan 3,*

Fuente: http://mdpi.com/



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