Concept Learning Using Vague Concept Lattice

Recently, the theory of Formal Concept Analysis is extensively studied with bipolar fuzzy setting for adequate analysis of vagueness in fuzzy attributes via a defined sharp boundary. However, many real life data sets contain vague attributes (i.e. beautiful, bald, and tadpole) which cannot be defined through a sharp or restricted boundaries. To process these types of data sets the current paper focused on disparate representation of vagueness in attributes through its evidence to support and reject. To achieve this goal, a method is proposed to generate vague concept lattice with an illustrative example.

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