Bipolar fuzzy concept learning using next neighbor and Euclidean distance

To handle the bipolarity in data with fuzzy attributes the properties of bipolar fuzzy set are introduced in the concept lattice theory for precise representation of formal fuzzy concepts and their hierarchical order visualization. In this process, adequate understanding of meaningful pattern existing in bipolar fuzzy concept lattice becomes complex when its size becomes exponential. To resolve this problem, the current paper proposes two methods based on the properties of next neighbors and Euclidean distance with an illustrative example. It is also shown that the proposed method provides similar knowledge extraction when compared to available subset-based method drawing for bipolar fuzzy concept lattice.