Description: The K-means model is a statistical model that represents the clustering of data points into K distinct groups. This unsupervised learning method aims to divide a dataset into K groups, where each group is characterized by the proximity of data points to a centroid, which is the average of the coordinates of the points in that group. The choice of the number K is crucial, as it determines how many groups will be formed. The algorithm starts by randomly assigning points to K groups and then iterates between two steps: updating the centroids and reassigning the points to the nearest groups. This process is repeated until the centroids no longer change significantly or a maximum number of iterations is reached. K-means is widely used due to its simplicity and efficiency, making it a popular tool in data analysis, market segmentation, and image compression. However, its performance can be affected by the choice of K and the presence of outliers, which can lead to undesired groupings. Despite its limitations, the K-means model remains a fundamental technique in the field of machine learning and data analysis, providing a foundation for more complex clustering methods.
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 J. MacQueen in 1967. Since then, it has been the subject of numerous research studies and improvements, adapting to different contexts and applications in data analysis.
Uses: K-means is used in various applications, such as customer segmentation in marketing, image analysis, data compression, and document clustering. It is also applied in biology for species classification and in identifying patterns in large datasets.
Examples: A practical example of K-means is its use in customer segmentation, where consumers with similar purchasing behaviors are grouped to tailor marketing strategies. Another example is in image compression, where color groups are used to reduce the amount of information needed to represent an image.
