A Hierarchical ST-DBSCAN with Three Neighborhood Boundary Clustering Algorithm for Clustering Spatio–temporal Data

Abstract

Clustering Spatio-temporal data is challenging because of the complexity of processing the spatial and temporal aspects. Various enhanced clustering approaches, such as partition-based and hierarchical-based algorithms have been proposed. However, the ST-DBSCAN density-based algorithm is commonly used to process irregularly shaped clusters. Moreover, ST-DBSCAN considers neighborhood parameters as spatial and non-spatial. The preliminary results from our experiments indicate that the ST-DBSCAN algorithm addresses temporal elements less effectively. Therefore, an improvement to the ST-DBSCAN algorithm was proposed by considering three neighborhood boundaries in neighborhood function. This experiment used the El Niño dataset from the UCI repository. The experimental results show that the proposed algorithm increased the performance indices by 27% compared to existing approaches. Further improvement using the hierarchical Ward's method (with thresholds of 0.3 and 0.1) reduced the number of clusters from 240 to 6 and increased performance indices by up to 73%. It can be concluded that ST-HDBSCAN is a suitable clustering algorithm for Spatio-temporal data.