K-Means Clustering in Machine Learning – a Review
Manu Mitra · Zenodo (CERN European Organization for Nuclear Research) · 2019
K means clustering is unsupervised machine learning algorithm. It aims to partition n observations into k clusters where each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a portioning of the data space into Voronoi cells. In this review paper a sample data from UCI is taken and K-means algorithm is applied on Iris data set. Multi class logistic regression is also performed to compare its performance. Trained resultant model graph for K-means clustering is plotted. Score Model graphs such as F1 log scale, frequency log scale, cumulative distribution, probability density of multiclass logistic regression for “Iris-setosa” are plotted to view its performance Score Model graphs F1 log scale, frequency log scale, cumulative distribution, and probability density of multiclass logistic regression for “Iris-versicolor” are plotted to view its performance.