Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets
AbstractThe 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
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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.
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.Chicago/Turabian Style
Cunjin, Xue; Xiaohan, Liao. 2017. "Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets." ISPRS Int. J. Geo-Inf. 6, no. 5: 139.