DBSTC: an effective method for discovering cluster features with different spatiotemporal densities |
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| Authors: | Zhenhong Du Yuhua Gu Chuanrong Zhang Feng Zhang Renyi Liu Jean Sequeira |
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| Institution: | 1. Geographic Information Science Institution, School of Earth Sciences, Zhejiang University, Hangzhou, People’s Republic of China;2. Zhejiang Key Laboratory of Geographic Information System, Zhejiang University, Hangzhou, People’s Republic of China;3. Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA;4. Zhejiang Key Laboratory of Geographic Information System, Zhejiang University, Hangzhou, People’s Republic of China;5. Department of Computer Science, Laboratoire des Sciences de l’Information et des Systemes (LSIS), Aix-Marseille University, Marseille, France |
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| 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. |
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| Keywords: | Data mining spatiotemporal clustering density-based clustering ordered reachable time window distribution shared nearest neighbor |
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