One attribute will be used as the diagnostic goal, e.g. faulty or operational, and the CBR will attempt to partition a training set of data with maximum accuracy. Critical factors in the success of this type of CBR are the amount of data available for the system to learn from, and the existence of particular properties in the data itself. Terminology Matching algorithms calculate the degree of match between the attributes of two cases. The closest matching cases to a given case are retained for further analysis. The match score may be biased by weighting attributes.
Diagnostic algorithms determine the classification of a case, given a set of retrieved cases.
Learning algorithms optimise the weights of attributes to increase classification accuracy. AIAI has carried out projects using CBR techniques, and has tools available for download.