A decision support method, based on bounded rationality concepts, to reveal feature saliency in clustering problems

In many real‐life data mining problems, there is no a‐priori classification (no target attribute that is known in advance). The lack of a target attribute (target column/class label) makes the division process into a set of groups very difficult to define and construct. The end user needs to exert considerable effort to interpret the results of diverse algorithms because there is no pre‐defined reliable ‘benchmark’. To overcome this drawback the current paper proposes a methodology based on bounded‐rationality theory. It implements an S‐shaped function as a saliency measure to represent the end user's logic to determine the features that characterize each potential group. The methodology is demonstrated on three well‐known datasets from the UCI machine‐learning repository. The grouping uses cluster analysis algorithms, since clustering techniques do not need a target attribute.