1 ) A computer implemented method for identifying contour groupings, within contour maps, and within at least one learning contour identification system, comprising: prepare at least one learning contour identification system for processing data types that are internal, and retrieving data types that are both internal and external, with file type format being external containers of data format described by data format in information technology, and where reading data types of whether data recalled was from internal or external format of the data type is dependent upon what stage the learning contour system resides in method execution, provide training cases of data instances of format numerical data type for at least one learning contour identification system iteratively reading and processing same, or converting said case to a system readable plurality of formatted data types for same system purpose, transform at least one of the training cases into at least one contour map, of at least one contour, with each contour of the mapping further transformed into having at least one training contour pattern metric set, each defined entirely between two memory addresses when stored, with each contour a contour pattern metric set containing a possibility of at least one: plurality label sets, plurality coordinate point sets, plurality statistical outcome point sets, plurality calculated outcome point-sets, plurality metric instruction code-sets, and plurality of grouping contours and mappings and their sub-pattern metric sets of same, store and label each metric of each contour into individual memory addressed locations, wherein managing appending to and removal from the memory being as determined necessary by at least one learning contour identification system's pattern identification process, retrieve from memory, iteratively, a portion of the total finite set of stored training contour pattern metric sets, each training contour pattern metric set retrieved for the purpose of grouping contour pattern metric sets for determining a black boxed or rule-based machine instruction code set, for the classifier of at least one learning contour identification system, that when the instruction code set is tested against the remaining set of labeled and known training contour pattern metric sets, a desired level of performance presented by a confusion matrix is achieved, store instruction code set and label as a black boxed or rule-based learned instruction set sequence, and store confusion matrix values, provide test cases of data instances of format numerical data type for at least one learning contour identification system iteratively reading and processing same, or converting said case to a system readable plurality of formatted data types for same system purpose, transform at least one of the test cases into at least one contour map, of at least one contour, with each contour of the mapping further transformed into having at least one test contour pattern metric set, each defined entirely between two memory addresses when stored, with each contour a contour pattern metric set containing a possibility of at least one: plurality label sets, plurality coordinate point sets, plurality statistical outcome point sets, plurality calculated outcome point sets, plurality metric instruction code sets, and plurality of grouping contours and mappings and their sub-pattern metric sets of same, store and label each metric of each contour into individual memory addressed locations, wherein managing appending to and removal from the memory being as determined necessary by at least one learning contour identification system's pattern identification process, retrieve form memory the black boxed or rule-based labeled instruction code set, determined from the learning contour identification system, and retrieve from memory in an iterative process, test contour pattern metrics, to finalize the identification of the unknown test labeled contour pattern metric set combinations optimized in training and captured in the instruction set used to identify contour pattern of interest, label at least one matched contour pattern metric set as an data item group of interest and compare performance to confusion matrix performance and repeat training and testing with increases or decreases in the number of contours in either test or training transformations, or both, and stop iterations of increases in contours when maximum percentage of success is achieved based on training confusion matrix performance readings, output to display interfaces the identification of the test contour pattern of the classifier, and output the success reading for that classification from the confusion matrix along with other information pertinent to understanding output by user.