Adaptive Tessellation Mapping (ATM) for Spatial Data Mining
Ting Wang · International Journal of Machine Learning and Computing · 2014
In the research of spatial data mining, gridding/tessellation mapping is a common technique to aggregate the locational data points in smaller regions (namely grids or tiles) so that properties of those data points can be observed.It is a natural way to study spatial related information because such information is dependent to the locational proximity in most of the cases, and it significantly reduces the effort needed to learn useful insights from the data of the entire area.In this work, we propose an adaptive tessellation mapping (ATM) method to decompose the entire area of interest to tiles with variable sizes so that spatial data mining can be carried out more efficiently, purposefully and dynamically.In particular, we show that human behavior can be understood better with ATM with some examples.