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Self-organizing map(SOM) have been widely applied in clustering, this paperfocused on centroids of clusters and what they reveal. When the input vectorsconsists of time, latitude and longitude, the map can be strongly linked tophysical world, providing valuable information. Beyond basic clustering, anovel approach to address the temporal element is developed, enabling 3D SOM totrack behaviors in multiple periods concurrently. Combined with adaptationstargeting to process heterogeneous data relating to distribution in time andspace, the paper offers a fresh scope for business and services based ontemporal-spatial pattern.

Analysis of Massive Heterogeneous Temporal-Spatial Data with 3D Self-Organizing Map and Time Vector