Description: K-means clustering is a vector quantization method widely used in cluster analysis within data mining. This algorithm aims to group a dataset into K groups or clusters, where K is a user-defined number. The technique is based on minimizing the variance within each cluster, meaning that data points within the same cluster are as similar as possible to each other, while clusters are as different as possible from one another. The process begins with the random selection of K centroids, which are the central points of each cluster. Then, each data point is assigned to the cluster whose centroid is closest, using a distance measure, commonly Euclidean distance. Subsequently, the centroids are recalculated as the average of all points assigned to each cluster, and the process is repeated until there are no significant changes in the assignments. This method is valued for its simplicity and efficiency, making it a popular choice for data segmentation in various applications, from marketing to image analysis and performance optimization across different digital platforms.
History: The K-means algorithm was first introduced by Hugo Steinhaus in 1956, although its popularity grew in the 1960s with the work of James MacQueen, who formalized the method. Since then, it has evolved and adapted to various applications in the fields of statistics and data mining.
Uses: K-means is used in various fields, including market segmentation, image analysis, data compression, and performance optimization. In a broader context, it is applied to enhance user experience by personalizing content and recommendations based on user behavior across different digital environments.
Examples: A practical example of K-means in performance optimization is its use in user segmentation for digital marketing campaigns. By grouping users based on their browsing behavior, companies can target more relevant ads and improve conversion rates.
