Visualized spatiotemporal data mining in investigation of Urmia Lake drought effects on increasing of PM10 in Tabriz using Space-Time Cube (2004-2019)

2021 ◽  
pp. 103399
Author(s):  
Hamed Ahmadi ◽  
Meysam Argany ◽  
Abolfazl Ghanbari ◽  
Maryam Ahmadi
Keyword(s):  
Data Mining ◽  
Space Time ◽  
Urmia Lake ◽  
Spatiotemporal Data ◽  
Spatiotemporal Data Mining ◽  
Drought Effects

Querying and Reasoning for Spatiotemporal Data Mining

2008 ◽  
pp. 335-374 ◽  
Author(s):  
G. Manco ◽  
M. Baglioni ◽  
F. Giannotti ◽  
B. Kuijpers ◽  
A. Raffaetà ◽  
...  
Keyword(s):  
Data Mining ◽  
Spatiotemporal Data ◽  
Spatiotemporal Data Mining

An ArcGIS add-in for spatiotemporal data mining in climate data

2019 ◽  
Vol 13 (1) ◽  
pp. 185-190
Author(s):  
Jisheng Xia ◽  
Jinne Li ◽  
Pinliang Dong ◽  
Kecheng Yang
Keyword(s):  
Data Mining ◽  
Spatiotemporal Data ◽  
Climate Data ◽  
Spatiotemporal Data Mining

scholarly journals Unsupervised Topographic Learning for Spatiotemporal Data Mining

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Guénaël Cabanes ◽  
Younès Bennani
Keyword(s):  
Data Mining ◽  
Radio Frequency Identification ◽  
Spatiotemporal Data ◽  
Rfid Technology ◽  
Spatiotemporal Information ◽  
Behavioral Studies ◽  
Spatiotemporal Data Mining ◽  
Efficient Data ◽  
Frequency Identification ◽  
Mining Tool

In recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning.

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