DBSTC: an effective method for discovering cluster features with different spatiotemporal densities

Autor: Zhenhong Du, Yuhua Gu, Chuanrong Zhang, Feng Zhang, Renyi Liu, Jean Sequeira
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: International Journal of Digital Earth, Vol 11, Iss 6, Pp 609-634 (2018)
Druh dokumentu: article
ISSN: 1753-8947
1753-8955
17538947
DOI: 10.1080/17538947.2017.1338765
Popis: Spatiotemporal clustering is one of the most advanced research topics in geospatial data mining. It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set. This paper presents an effective density-based spatiotemporal clustering algorithm (DBSTC). First, we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood, which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods. Second, we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities. The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets. In addition, practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.
Databáze: Directory of Open Access Journals
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