A Grid and Density Based Adaptive Clustering Algorithm for Spatio-Temporal Data Mining
Keywords:
Clustering, core points, seismology, spatio-temporal data, pattern miningAbstract
The Indian subcontinent experiences seismic activities which are visualized in India’s Seismic map. These seismic spatio-temporal characteristics need to analyze to understand the evolution. Clustering is a machine learning technique to highlight the patterns of grouping similar objects in the spatio-temporal dimensional. Our research work in this paper proposes a novel algorithm to analyse the spatio-temporal data for patterns through clustering. This is a hybrid method based on grid and density clustering. We have devised a method to find the required total number of core points for density clustering. The efficiency of our algorithm is higher due to appropriate selection of core points with respect to the density in the region. In addition, proposed algorithm requires minimal user defined parameters and minimizes Euclidean distance computation to the neighboring core points in the current region and not with all of the core points. The algorithm has been experimentally tested for correctness of results and performance. It is observed from the results, the Earthquake spatio-temporal data has clustering tendency and the events indicate higher correlation with respect to frequency and time. The quality of clustering is effective and efficient with the silhouette index 0.93.
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