Description: K-means clustering is an unsupervised machine learning technique that aims to divide a dataset into K groups or clusters, where each group contains similar elements to each other and different from those in other groups. This method is based on minimizing the variance within each cluster, meaning that data points within the same group are as close as possible to the group’s centroid, which is the average of all points in that cluster. The algorithm starts by randomly selecting K initial centroids and then assigns each data point to the cluster whose centroid is closest. Subsequently, the centroids are recalculated, and the process is repeated until there are no significant changes in the assignment of points to clusters. This approach is particularly useful for exploring the structure of data and for market segmentation, as it allows for the identification of hidden patterns and relationships in large volumes of information. However, the choice of the number K is crucial and can influence the results, often requiring the application of additional methods to determine the optimal number of clusters.
History: The K-means algorithm was first introduced by Hugo Steinhaus in 1956, although its popularity grew in the 1960s when it was formalized by James MacQueen in 1967. Since then, it has been widely used in various disciplines, including statistics, machine learning, and data mining. Over the years, variations and improvements of the original algorithm have been developed, such as K-medoids, which address some of the limitations of the K-means method, such as sensitivity to outliers.
Uses: K-means clustering is used in a variety of fields, including marketing for customer segmentation, biology for species classification, and in image processing. It is also applied in anomaly detection and in organizing large volumes of data, facilitating the identification of patterns and trends.
Examples: A practical example of using K-means is in analyzing customers of an online store, where users can be grouped based on their purchasing behaviors to personalize offers. Another example is in image segmentation, where K-means can be used to identify different regions in a photograph, such as sky, water, and land.
