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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol. 4(6): 478-482 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.458

Adaptive Tessellation Mapping (ATM) for Spatial Data Mining

Ting Wang
Abstract—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.

Index Terms—Spatial data mining, data structure, adaptive tessellation mapping, behavioral analysis.

Wang Ting is with SAP Asia Pte Ltd, Singapore (e-mail : dean.wang@sap.com).


Cite: Ting Wang, "Adaptive Tessellation Mapping (ATM) for Spatial Data Mining," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 478-482, 2014.

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