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Multi-level association rules and directed graphs for spatial data analysis
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DOI：10.1016/j.eswa.2013.03.004.png)
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Abstract
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We propose a methodology that upgrades the methods of the Lagrangian analysis of surface sea-water parcels. This methodology includes data mining with efficient visualization techniques, namely, spatial-temporal association rules and multi-level directed graphs with different levels of space and time granularity. In the resulting multi-level directed graphs we can intertwine knowledge from various disciplines related to oceanography (in our application) and perform the mining of such graphs. We evaluate the proposed methodology on Lagrangian tracking of virtual particles in the velocity field of the numerical model called the Mediterranean Ocean Forecasting Model (MFS). We describe an efficient algorithm based on label propagation clustering, which finds cycles and paths in multi-level directed graphs and reveals how the number and size of the cycles depend on the seasons. In addition, we offer three interesting results of the visualization and mining of such graphs, that is, the 12 months periodicity of the exchange of water masses among sea areas, the separation of Mediterranean Sea circulation in summer and winter situations, obtained with the hierarchical clustering of multi-level directed graphs, and finally, with visualization with multi-level directed graphs we confirm the reversal of sea circulation in the Ionian Sea over the last decades. The aforementioned results received a very favorable evaluation from oceanographic experts. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Spatial data mining
Lagrangian analysis
Spatial-temporal association rules
Multi-level directed graphs
Oceanography
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