Kmeansmenetelmä

The k-means algorithm is a popular unsupervised machine learning method used for partitioning a dataset into k distinct, non-overlapping clusters. The goal of the algorithm is to group similar data points together. It operates iteratively to assign each data point to one of the k clusters based on its features.

The process begins by randomly selecting k initial centroids, which represent the centers of the clusters. Next, each data point in the dataset is assigned to the nearest centroid. The "nearest" is typically determined by calculating the Euclidean distance between the data point and each centroid. After all data points are assigned, the algorithm recalculates the position of each centroid by taking the mean of all the data points assigned to that cluster.

These steps of assigning data points to centroids and then recalculating centroids are repeated until the centroids no longer move significantly, or until a predefined number of iterations is reached. The output of the k-means algorithm is a set of k clusters, with each data point belonging to exactly one cluster, and the final positions of the k centroids. The value of k, the number of clusters, must be specified by the user before running the algorithm. It is a widely used technique in various applications such as image compression, document clustering, and customer segmentation.