A Novel Algorithm to Spatio-Temporal Data Clustering on Indian Earthquake Dataset

Abstract

Abstract The seismic map of India displays the Himalayas, the North-East and the Andaman-Nicobar Islands are highly seismically active regions. The characteristics of the seismicity of Indian sub-continent needs to analyzed. This paper presents a novel algorithm to analyse data through partitioning by forming clusters. The clusters of spatial and spatio-temporal data are generated by distributing the data in spatial buckets or bins, finding the neighbouring buckets, and reducing the computation of distance. Moreover, centroid selection method focuses on randomly selecting centroids, based on the density of data in the spatial region. The advantage of the algorithm is, it is simpler in design and one parameter settings required. The result indicates that the approach is effective in detecting spatio-temporal patterns as clusters on the earthquake catalogue dataset. The experiments demonstrate the regions with higher occurrence of earthquake events, have more clusters formed depicting the earthquake prone areas. The clustering quality measured by Silhouette index is in the range of 0.88 to 0.93, which reflects good clusters are formed.