Paper Info
Title
DBSTC: an effective method for discovering cluster features with different spatiotemporal densities.
Abstract
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.
Year
DOI
Venue
2018
10.1080/17538947.2017.1338765
INTERNATIONAL JOURNAL OF DIGITAL EARTH
Keywords
Field
DocType
Data mining,spatiotemporal clustering,density-based clustering,ordered reachable time window distribution,shared nearest neighbor
Geospatial analysis,k-nearest neighbors algorithm,Data mining,Cluster (physics),Data set,Information data,Estimation of distribution algorithm,Effective method,Cluster analysis,Mathematics
Journal
Volume
Issue
ISSN
11.0
6.0
1753-8947
Citations 
PageRank 
References 
1
0.37
15
Authors
6
Name
Order
Citations
PageRank
Zhenhong Du13116.98
Yuhua Gu210.37
Chuanrong Zhang317019.67
Feng Zhang4127.66
Liu Renyi51513.13
J. Sequeira6419.00