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.
Credit scoring is a system used to rate credits and thus try to automate the decision making process at the time of purchasing a loan, and to [...]
Read More »Big data analytics is the process of analyzing large and complex data sources to uncover trends, patterns, customer behaviors, and other data sources [...]
Read More »Fraud detection software is an important tool for protecting companies and individuals from fraudulent activity and minimizing the risk of fraud.
Read More »The use of Artificial Intelligence in business is becoming more and more common and necessary for the optimization and evolution of processes. In one of our [...]
Read More »
