K-MEAN-clustering-with-mathematics-

This is a unsupervised Algo which makes n different cluster based on different features of samples it calcuates distence between each sample with the centers( defined as the n clusters center ) and update their centers as the n cluster exact center when distance get calculated it find the minimum distance from all center of the clusters and the center of the cluster having minimum distance have to updated as the average of current center position and the center position of the sample so at the last when centers of the clusters calculated as final Now we can provide new sample to get which class this sample belong according the minimum distance between cluster said to be belonging class of sample . I used euclidean distance formula to calculate distance but we have availabel 4 types of formulas to calculate distance 1 euclidean distance/ squre_root distance 2 squared euclidean distance 3 cosine distance 4 manhattam distance