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

by 1,2 and 3,*
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
ISPRS Int. J. Geo-Inf. 2017, 6(5), 139; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050139
Received: 16 February 2017 / Revised: 11 April 2017 / Accepted: 25 April 2017 / Published: 30 April 2017
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
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MDPI and ACS Style

Cunjin, X.; Xiaohan, L. Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets. ISPRS Int. J. Geo-Inf. 2017, 6, 139. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050139

AMA Style

Cunjin X, Xiaohan L. Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets. ISPRS International Journal of Geo-Information. 2017; 6(5):139. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050139

Chicago/Turabian Style

Cunjin, Xue, and Liao Xiaohan. 2017. "Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets" ISPRS International Journal of Geo-Information 6, no. 5: 139. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi6050139

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