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We present a clustering algorithm that classifies the points of a dataset by a combination of scalar variables' values as well as spatial locations. How heavily the spatial locations impact the algorithm is a tunable parameter. With no impact the algorithm bins the data by calculating a histogram and classifies each point by a bin ID. With full impact, points are bunched together by spatial neighborhood regardless of value. This approach is unsurprisingly very sensitive to this weighting; adoi:10.1016/j.procs.2015.05.497 fatcat:347sjjf6e5beppxgnixudhmkum