Paper Details

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

Ravi D Patel, Bhavesh Tamawala, Kirti Sharma, “Discovery and Analysis of Ocean Climate Indices Using DSNN Clustering Algorithm”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 1, Issue 5, pp. 679-687, May 2014.