Mathematical Approach to Representation of Locations Using K-Means Clustering Algorithm

Abstract

In artificial intelligence (AI), we will be able to handle a large amount of data without the need for human interaction in an efficient manner. The K-means clustering technique may be used to learn from unsupervised data in a straightforward manner. Machine learning and data mining both benefit from this method's ability to handle large datasets. K-means clustering uses t iterations to compute the outcomes of n items in k clusters. A variety of apps are employed in nearly every field of study to provide an uninterrupted, easy, and effective means of data learning. To illustrate how the K-means clustering method works, I'm going to use a mathematical way to describe the locations in a real-world dataset.