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

Fitrianah, Devi ORCID: https://orcid.org/0000-0001-5029-8149, Fahmi, Hisyam ORCID: https://orcid.org/0000-0002-2665-1536, Hidayanto, Achmad Nizar ORCID: https://orcid.org/0000-0002-5793-9460 and Arymurthy, Aniati Murni (2022) Improved partitioning technique for density cube-based spatio-temporal clustering method. Journal of King Saud University - Computer and Information Sciences. ISSN 1319-1578 (In Press)

Preview

Text 11941.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview

Abstract

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 algorithm also outperformed the baseline algorithms (ST-DBSCAN, AGRID+, and ST-AGRID) in labeling accuracy for the storm data set, yielding results of 82.68%, 38.36%, 76.13%, and 78.66%, respectively.

Item Type:	Journal Article
Keywords:	clustering; spatio-temporal clustering; density-cube spatio-temporal clustering; partitioning technique; Imstagrid
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Hisyam Fahmi
Date Deposited:	09 Nov 2022 10:51

Downloads

Origin of downloads

Actions (login required)

View Item