The A FUZZY BASED CLUSTER IMPROVEMENT ANALYSIS BY USING CLUSTERING WITH NEUTROSOPHIC LOGIC

Abstract

Fuzzy C-means has been utilized successfully in a
wide range of applications, extending from the
clustering capability of the K-means to datasets that
are uncertain, vague and otherwise are hard to be
clustered. In cluster analysis, certain features of a
given data set may exhibit higher relevance in
comparison to others. To address this issue, FeatureWeighted Fuzzy C-Means approaches have emerged
in recent years. However, there are certain
deficiencies in the existing methods, e.g., the
elements in a feature-weight vector cannot be
adaptively adjusted during the training phase, and the
update formulas of a feature-weight vector cannot be
derived analytically. In this study, an Improved
Feature-Weighted Fuzzy C-Means is proposed to
overcome to these shortcomings.