Abstract
This Paper based on finding interesting spatio-tempral pattern from Earth Science data. The data consists measurements of various Earth Science variables (include Temperature and pressure) which are related with time series. Earth Science data has strong seasonal components that needs to be removed prior to pattern analysis, as the Earth Scientist are primarily interested in pattern that represent deviation from normal seasonal variations such as anomalous climate event (e.g. , E1 Nino) or tends (e.g., global warming). We used monthly Z Score to remove seasonality. After processing, we apply DSNN clustering algorithm to cluster the temperature time series associated with point on the ocean, yielding clusters that represent ocean regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of these ocean areas and thus, represent potential OCIs (Ocean climate indices).To evaluate cluster centroid for their usefulness, we must determine which cluster centroids significantly influence the land area. For this task, we use variety approaches that analyze the correlation between potential OCIs and time series.
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