K-means is a clustering algorithm used in the field of unsupervised learning. The objective of the algorithm is to group a set of data into K clusters, where K is a predefined number of clusters. The algorithm starts by selecting K centroids at random and assigning each data point to the nearest centroid. Then, the algorithm recalculates the centroids as the average of all data points assigned to each centroid, and repeats the process of assigning and recalculating centroids until convergence is reached and the centroids no longer change position significantly. As a result, the data space is divided into K Voronoi cells (one per centroid), and each input observation can be associated with the nearest centroid. The K-means algorithm is widely used in customer segmentation, text classification and image processing, among other applications.
There is a consensus among executives of the world's leading companies about the crucial impact that Artificial Intelligence (AI) will have on the [...]
Read More »In this article we are going to focus on how artificial intelligence (AI) can increase efficiency and reduce costs for your company by [...]
Read More »Chargeback refers to refunds that occur when, at the request of a cardholder, the bank requests a refund on his or her behalf [...].
Read More »Unlike a computer program, in which a list of commands are processed through a computer program, AI goes beyond the [...]
Read More »
