A Review on Analysis of K-Means Clustering Machine Learning Algorithm based on Unsupervised Learning

Abstract

The process of machine learning is understood within Artificial Intelligence. Machine learning process gives the tools the ability to learn from their experiences and improve themselves without any coding. In machine learning, we program a computer or machine in such a way that the user wants the work done by the machine. It can give such work and in this process the computer does its work on the basis of the data already with it and gives its performance. The objective of writing the paper is how K- Means clustering algorithm is applied on the model dataset based on unsupervised learning. We used to pass feature data and label data to machine learning model in the supervised learning. But the method of unsupervised learning algorithms is different. In this we do not give the feature data and target data to the model. The dataset model uses only the input data for processing and the output data has no meaning in the model. Accordingly on the basis of the similarities found in the data and model predict the desired output. K-means is clustering algorithm based on unsupervised learning in which data and objects are separated into different clusters in such a way that objects that have similar properties are put in one cluster and objects that have different properties are put in separate cluster.