Description: The K-Means Clustering Method is an unsupervised learning technique that aims to group a dataset into K groups or clusters, where K is a predefined number set by the analyst. This method is based on minimizing the variability within each cluster and maximizing the variability between clusters. It uses algorithms that assign each data point to the nearest cluster, calculating the distance between points and the centroids of the clusters. As iterations are performed, the centroids are adjusted to better reflect the data distribution. This approach is particularly useful in situations where there is no prior information about the categories of the data, allowing for the discovery of hidden patterns and structures in large volumes of information. K-Means is widely used in various fields, such as market analysis, customer segmentation, computational biology, and image compression, among others. Its simplicity and effectiveness make it a popular tool for data exploration and decision-making based on patterns.
History: The K-Means method was first introduced by Hugo Steinhaus in 1956 and later formalized by J. MacQueen in 1967. Since then, it has evolved and become one of the most widely used algorithms in data analysis. Over the years, various variations and improvements of the original algorithm have been developed, including methods for determining the optimal number of clusters and techniques for handling high-dimensional data.
Uses: The K-Means method is used in various applications, such as customer segmentation in marketing, where it helps identify groups of consumers with similar behaviors. It is also applied in biology to classify species or genes, in image processing to reduce file sizes, and in anomaly detection in security systems.
Examples: A practical example of using K-Means is in analyzing customer data from an online store, where users can be grouped based on their purchasing patterns. Another example is in the field of biology, where it can be used to group different plant species based on morphological characteristics.
