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- W4309227923 abstract "Cluster analysis is a machine learning technique that identifies patterns in a given dataset. This study uses partition-based clustering techniques to perform unsupervised seismic event classification based on spatial location, magnitude and depth. A global earthquake catalogue dataset from 1970 to 2014 is being used. We investigate five partition-based clustering techniques, namely k-means, Partition Around Medoids (PAM), k-means++, Clustering Large Applications (CLARA) and Fuzzy c-means, using three widely used validation indices namely Sum of Squared Errors (SSE), Davies-Bouldin Index (DBI), and Dunn Index (DI). The attributes considered in this context are longitude, latitude, magnitude, and depth. So, in the experimental analysis based on validation indices, k-means has outperformed its competitors. Similarly, k-means++ comes second among its competitors." @default.
- W4309227923 created "2022-11-24" @default.
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- W4309227923 date "2022-09-23" @default.
- W4309227923 modified "2023-09-24" @default.
- W4309227923 title "Analysis of Seismic Data Using Partition-Based Clustering Techniques" @default.
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- W4309227923 doi "https://doi.org/10.1109/globconpt57482.2022.9938362" @default.
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