Fuzzy C-Mean Image Clustering Using LDA and Infinite Feature Selection

Abstract

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters. Fuzzy c-mean is the most popular and commonly used technique for image clustering. FCM algorithm is a distinctive clustering algorithm and it makes use of the squared-norm to determine the similarity between prototypes and data points. Furthermore, several algorithms are developed by numerous authors based on the FCM with the aim of clustering more general dataset. In this research work, features are classified with the help of linear discriminant analysis technique. Then after features representation, the features are ranked by using infinite feature selection approach, in which least rank feature which would not make any contribution of algorithm are neglected. After that image clustering is done on the higher ranked features thus decreasing the computational load of the program and improving the efficiency of the algorithm. The performance parameter like accuracy, sparseness and time complexity will be measured after stimulating proposed algorithm.