Description: The K-means clustering algorithm is an unsupervised learning technique used to partition a dataset into k distinct clusters. Each cluster is defined by its centroid, which is the mean of all observations belonging to that cluster. The main objective of the algorithm is to minimize the variation within each cluster and maximize the variation between clusters. To achieve this, the algorithm follows an iterative process that begins with the random selection of k initial centroids. Then, each observation is assigned to the cluster whose centroid is closest, using a distance measure, commonly the Euclidean distance. After assigning all observations, the centroids are recalculated as the mean of the observations in each cluster. This process is repeated until the centroids no longer change significantly or a maximum number of iterations is reached. K-means is known for its simplicity and efficiency, making it a popular choice for segmentation and data analysis tasks in various applications, including data mining and statistical analysis, where it is used to group similar data points, facilitating tasks such as pattern recognition and customer segmentation.
History: The K-means algorithm was introduced in 1957 by statistician Hugo Steinhaus and later popularized by James MacQueen in 1967. Since then, it has evolved and become one of the most widely used methods in data analysis and machine learning. Over the years, various variations and improvements of the original algorithm have been developed to address its limitations, such as sensitivity to the choice of initial centroids and convergence to local solutions.
Uses: K-means is used in a variety of applications, including image segmentation, market analysis, data compression, and document clustering. In data analysis, it is commonly employed for grouping similar data points to reveal underlying patterns, facilitating tasks such as exploratory data analysis and customer segmentation.
Examples: A practical example of K-means in data analysis is its use in customer segmentation, where similar customer profiles are grouped to assist in targeted marketing strategies. Another example is in organizing large datasets, where K-means can group similar records to improve data retrieval and processing efficiency.
