1. A data mining method comprising: providing two or more multi-dimensional data sets; providing a plurality of data points for at least a first multi-dimensional data set of the two or more multi-dimensional data sets, wherein each data point is representative of at least two parameters associated with an event of a plurality of events; grouping the plurality of data points into one or more multi-dimensional groups of clustered data points, wherein each of the one or more multi-dimensional groups is defined by one or more bin boundaries associated with the at least two parameters; providing a plurality of subtraction data points for at least a second multidimensional data set of the two or more multi-dimensional data sets, wherein each subtraction data point is representative of the at least two parameters associated with an event of a plurality of events; grouping the plurality of subtraction data points into one or more groups of subtraction data points based on the one or more bin boundaries of the one or more multi-dimensional groups of clustered data points, wherein each of the one or more groups of subtraction data points corresponds to one of the one or more multidimensional groups of clustered data points; and comparing the plurality of subtraction data points of one or more groups of subtraction data points to corresponding multi-dimensional groups of clustered data points to determine similarities or differences therebetween.