Object detection using K-means clustering - A research

Abstract

Clustering is an unsupervised machine learning technique and the task is to group a set of objects in such a way that objects in the same group are more similar to each other than those in other groups. There are different clustering techniques, each with its own advantages and disadvantages. The K-means clustering algorithm is our main focus in this paper. K-means is mostly used when there is a large number of unlabeled data. The difficulties in the path of K-means clustering are a) Different initial points can lead to different final clusters. b) It does not work with clusters of different sizes and densities. c) With the global cluster, it does not work well and is difficult to determine K Value. Our main aim is to try and modify the K-means clustering algorithm to get rid of the above-mentioned drawbacks.