Description: K-means clustering is a machine learning method used to group a dataset into K distinct clusters, where K is a predefined number. This algorithm is based on the idea that data points that are closer to each other should be grouped together, thus forming clusters that share similar characteristics. The process begins by selecting K initial centroids, which are representative points for each cluster. Then, each data point is assigned to the cluster whose centroid is closest, using a distance measure, commonly the Euclidean distance. Once all points have been assigned, the centroids are recalculated as the mean of all points in each cluster. This assignment and recalculation process is iteratively repeated until the centroids no longer change significantly, indicating that convergence has been reached. K-means clustering is valued for its simplicity and efficiency, especially in large datasets, making it a popular tool in data analysis and data mining. However, its performance can be affected by the choice of the number of clusters K and the presence of outliers, requiring careful analysis before implementation.
History: The K-means algorithm was first introduced in 1957 by statistician Hugo Steinhaus, although its popularity grew in the 1960s when it was formalized by other researchers. Over the years, various variations and improvements of the original algorithm have been developed, adapting to different contexts and needs in data analysis.
Uses: K-means is used in a variety of fields, including marketing for customer segmentation, in biology for species classification, and in image processing for object segmentation. It is also common in data mining and pattern analysis, where the goal is to identify groups within large volumes of data.
Examples: A practical example of K-means is its use in customer segmentation in an e-commerce company, where users are grouped based on their purchasing habits. Another example is in image classification, where similar pixels can be grouped to identify different objects within an image.
