Description: The K-means clustering algorithm is an unsupervised learning technique used to partition a dataset into K clusters, where K is a predefined number. This method aims to minimize the variance within each cluster, meaning that data points within the same cluster are as similar as possible to each other, while the clusters themselves are as different as possible. 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 the Euclidean distance. Subsequently, the centroids are recalculated as the average of all points assigned to each cluster. This assignment and recalculation process is iteratively repeated until the centroids no longer change significantly or a maximum number of iterations is reached. K-means is valued for its simplicity and efficiency, making it a popular choice for data analysis in various domains, where identifying patterns and segmenting data are crucial.
History: The K-means algorithm was first introduced by statistician Hugo Steinhaus in 1956, 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 it to different contexts and types of data.
Uses: K-means is used in various fields, including marketing for customer segmentation, in biology for species classification, and in image processing for data compression. It is also common in data mining and pattern analysis.
Examples: A practical example of K-means is its use in customer segmentation in an online store, where users are grouped based on their purchasing behaviors. Another example is in identifying groups of genes with similar functions in genomic studies.
