A Modified Fuzzy C Means Clustering Using Neutrosophic Logic

A cluster can be defined as the collection of data objects grouped into the same group which are similar to each other whereas data objects which are different are grouped into different groups. The process of grouping a set objects into classes of similar objects is called clustering. In fuzzy c means clustering, every data point belongs to every cluster by some membership value. Hence, every cluster is a fuzzy set of all data points. Neutrosophic logic adds a new component "indeterminacy" to the fuzzy logic. In Neutrosophic Logic, the rule of thumb is that every idea has a certain degree of truthiness, falsity and indeterminacy which are to be considered independently from others. In our proposed algorithm, we have used Neutrosophic logic to add the indeterminacy factor in the Fuzzy C-Means Algorithm. We have modified the formula of calculating the membership value as well as the cluster center calculation and generated the clusters of documents as output.