A Modified Fuzzy C-Means Algorithm using Fruit Fly Optimization for Data Clustering

Clustering is the most promised technique used for data analysis. It is an unsupervised machine learning technique, means it does not require any target class. In the recent years so many clustering algorithms are invented with better accurate results. In this paper introduced a Modified Fuzzy C-Means Clustering algorithm based on Fruit-Fly optimization algorithm. Fruit-Fly optimization algorithm is used for extracting useful patterns from the data. Then our proposed approach will generate useful clusters. Our technique demonstrated to be an exceptionally effective method for the clustering of numerical data with high rate of accurateness. The function can be assessed in the name of sensitivity, specificity and accuracy. The accurateness can be associated with other prevailing methods.