Description: K-means clustering analysis is a data mining technique that aims to divide a dataset into groups or ‘clusters’ based on similar characteristics. This method is based on the idea that data can be grouped in such a way that elements within the same group are more similar to each other than to those in other groups. The process begins by selecting a predefined number of groups, known as ‘k’, and then assigning each data point to the group whose centroid (average of the coordinates of the points in the group) is closest. Through iterations, the centroids are recalculated, and data points are reassigned until group assignments stabilize. This technique is particularly useful in analyzing large volumes of data, where identifying patterns and trends can be complex. K-means is valued for its simplicity and efficiency, making it a popular choice in the field of data analysis and artificial intelligence, where processing and analyzing large amounts of information quickly and effectively is required.
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. Since then, it has evolved and adapted to various applications in data analysis and artificial intelligence.
Uses: K-means is used in various fields, including market segmentation, image analysis, data compression, and anomaly detection. Its ability to identify patterns in large datasets makes it valuable in behavior analysis and personalized services.
Examples: A practical example of K-means is its use in customer analysis for an online platform, where users are grouped based on their behaviors to provide personalized recommendations. Another example is in image segmentation, where similar pixels are grouped to enhance image quality.
