Improved partitioning technique for density cube-based spatio-temporal clustering method

This work proposes a novel partitioning technique on the density-cube-based data model for the Spatio-temporal clustering method. This work further adapts this clustering approach to Spatio-temporal data. We have compared the IMSTAGRID-the proposed algorithm to the ST-DBSCAN, AGRID+, and ST-AGRID algorithms and have found that the IMSTAGRID algorithm improves the data partitioning technique and the interval expansion technique and is able to achieve uniformity in the spatial and temporal dimensional values. Three types of Spatio-temporal data sets have been used in this experiment: a storm data set and two synthetic data sets - synthetic data set 1 and synthetic data set 2. Both the storm data set and synthetic data set 2 were comparable in terms of the scattering of the data points, while synthetic data set 1 contained clustered data. The performance of the IMSTAGRID clustering method was measured via a silhouette analysis, and its results surpassed the other algorithms investigated; the silhouette index for synthetic data set 2 was 0.970, and 0.993 using synthetic data set data set 1. The IMSTAGRID algo-rithm also outperformed the baseline algorithms (ST-DBSCAN, AGRID+, and ST-AGRID) in labeling accu-racy for the storm data set, yielding results of 82.68%, 38.36%, 76.13%, and 78.66%, respectively. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study proposes a novel partitioning technique for spatio-temporal clustering using the density-cube-based data model. The proposed IMSTAGRID algorithm improves data partitioning and achieves uniformity in spatial and temporal dimensional values. Experimental results show that the IMSTAGRID algorithm outperforms other algorithms in terms of clustering performance and labeling accuracy.