A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes

Abstract

Real-world data collections are often heterogeneous (represented by a set of mixed attributes data types: numerical, categorical and fuzzy); since most available similarity measures can only be applied to one type of data, it becomes essential to construct an appropriate similarity measure for comparing such complex. In this paper, a framework of new and… (More)
DOI: 10.1007/s00500-012-0974-6

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