Fuzzy Multivariate Outliers with Application on BACON Algorithm

Depending on a crisp cut-off value to identify outliers is not linguistically meaningful or insightful for reliable decision-making. In this paper, two methods of fuzzy treatment for the Blocked Adaptive Computationally-efficient Outlier Nominator (BACON) algorithm are proposed rather than a crisp cut-off threshold. Experimentation has shown that the proposed fuzzy treatments for BACON provide more meaningful interpretations to the final results than its crisp version and captured the uncertainty at the boundary between the inliers and outliers of the data.