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, Feature-Weighted 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.
In this work, clustering based image segmentation method used and modified by introducing
neutrosophic logic. The clustering technique with neutrosophy is used to deal with indeterminacy factor
of image pixels. The approach is to transform the image into the neutrosophic set by calculating truth,
falsity and indeterminacy values of pixels and then, the clustering technique based on neutrosophic set is
used for image segmentation. The clusters are then refined iteratively to make the image more suitable for
the segmentation. This iterative process converges when required number of clusters areformed. Finally,
the image in the
1. INTRODUCTIO