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

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A decision support method, based on bounded rationality concepts, to reveal feature saliency in clustering problems
Abstract: 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
Keywords:	Feature selection Feature saliency Data mining Cluster analysis Classification Bounded-rationality
Author(s):	.
Source:	Decision Support Systems 54 (2012) 292–303
Subject:	تصمیم گیری
Category:	مقاله مجله
Release Date:	2012
No of Pages:	12
Price(Tomans):	0
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