Kmeansalgoritmissa

Kmeansalgoritmissa, often referred to as the K-means algorithm, is a popular unsupervised machine learning algorithm used for partitioning a dataset into K distinct clusters. The primary goal is to group similar data points together while ensuring that dissimilar data points are in different clusters. It is an iterative algorithm that works by minimizing the within-cluster variance, often measured by the sum of squared distances between data points and their respective cluster centroids.

The algorithm begins by initializing K centroids, which are the initial representatives of each cluster. These initial centroids can be chosen randomly from the data points or using more sophisticated methods like K-means++. Once initialized, the algorithm proceeds in two main steps: assignment and update. In the assignment step, each data point in the dataset is assigned to the cluster whose centroid is closest to it, typically using Euclidean distance. In the update step, the positions of the K centroids are recalculated as the mean of all data points assigned to that cluster. These two steps are repeated iteratively until the centroids no longer move significantly, or a predefined number of iterations is reached, indicating convergence.

K-means is widely used in various applications such as image segmentation, customer segmentation, document clustering, and anomaly detection. Its simplicity and computational efficiency make it a go-to choice for many clustering tasks. However, the algorithm is sensitive to the initial placement of centroids, and the number of clusters K must be specified beforehand. Different initializations can lead to different clustering results, and choosing the optimal K is often done through methods like the elbow method or silhouette analysis.