Geostatistical cluster analysis is routinely applied for decomposition of mixed data sets, which contain samples with discrete spatial information that puts the data into a relevant geographical context. Various methods exist for this purpose; however, where the individual clusters are intertwined with irregular, discontinuous or complex geometries, conventional methods struggle or fail. Therefore, a new approach has been developed in JMP and implemented exclusively with JMP scripts. After an initial estimate of the statistical moments of the underlying components, a series of search trees are built through the sample grid and samples are allocated to one of the conceptual target populations, depending on their probability density functions. Thus the mixed data set is split into its components while maintaining the spatial relationship within and across individual clusters. This method has been developed for the mining industry to domain the phases of multistage mineralising events of complex ore bodies; but possible fields of application include virtually all disciplines of natural sciences (e.g., environmental research, hydrology, biology, agriculture, etc.) and every other discipline where the spatial position of the data matters (such as pattern recognition, image processing, logistics and marketing).