Most flow/image cytometric data analysis methods look for clusters in the data corresponding to specific cell subpopulations. Comparisons between different cytometry datafiles often use human pattern recognition visualization of all the different combinations of variables ("parameters") two at a time in so-called bivariate scattergrams. Not only is this tedious, but it can miss potential clusters due to projection of higher dimensional dataspaces down onto two dimensional planes making them indiscernible as separate clusters. Novel data mining algorithms, implemented in software allow for the comparison of two or more higher dimensional datafiles without the requirement for reduction of dimensionality for human visualization. Of equal importance is the comparison of higher dimensional clusters which may move around slightly in space, yet still be "similar" according to algorithms which provide measures of similarity. This software, written in C/C++ and currently implemented in software with a Windows graphical user interface, allows for direct reading of FCS2.0 format flow cytometry datafiles of any number of parameters. In a few minutes or less, complex multiparameter data of two or more files can be compared on a personal computer or workstation. The software operates in either supervised or unsupervised mode, depending on whether the user wishes to include prior user knowledge or in a data mining discovery mode. Differences between these files can be exported as sub-datafiles which can be further analyzed using any other software that can read FCS2.0 data format.