Title :
A Visual Approach for Spatio-Temporal Data Mining
Author :
Kechadi, M-Tahar ; Bertolotto, Michela
Author_Institution :
Sch. of Comput. Sci. & Informatics, Univ. Coll. Dublin
Abstract :
In this paper, we propose a system for mining very large spatio-temporal datasets. The system comprises new techniques to efficiently support the data-mining process, address the spatial and temporal dimensions of the dataset, and visualize and interpret results. In particular, we have developed an advanced visualization tool for flexible and intuitive interaction with the dataset, including functionality for displaying association rules and variable distributions
Keywords :
data mining; data visualisation; visual databases; association rule; knowledge discovery; spatio-temporal data mining; visualization tool; Algorithm design and analysis; Computer science; Data analysis; Data mining; Data visualization; Educational institutions; Engines; Informatics; Pattern analysis; Shape control;
Conference_Titel :
Information Reuse and Integration, 2006 IEEE International Conference on
Conference_Location :
Waikoloa Village, HI
Print_ISBN :
0-7803-9788-6
DOI :
10.1109/IRI.2006.252465