Cluster optimization using appropriate nearest neighbour

Abstract

In this postulation, presents the clustering of facial images using machine learning algorithm such as nearest neighbor and approximate rank order clustering. Clustering is a technique for classifying similar kind of object based on their trait. Clustering of images is challenging problems and there is still a considerable measure of work that needs to be done in this area. Problems in clustering large dataset is to choose the quantity of clusters and evaluating the obtained clusters. Clustering regard as the most important unsupervised learning as it manages finding a structure in an accumulation of unlabeled information. A loose meaning of clustering could be "the way toward sorting out articles into clusters whose people are nearby one means or another. A cluster in this manner is an accumulation of items which are "comparable" amongst them and are "divergent" to the articles which are place with different cluster. This thesis presents a work to improve clustering method to decrease the number of clusters in a LFW (Labeled face in wild) dataset. Previous work uses kd tree a nearest neighbor method and approximate rank order clustering method to find cluster on LFW dataset. our proposed method implement ball tree a better nearest neighbor algorithm to reduce the number of clusters created by previous method.