kkeskiarvomenetelmä

kkeskiarvomenetelmä, also known as the k-averaging method, is an iterative algorithm used in data analysis and machine learning for partitioning a dataset into k distinct clusters. The core idea is to assign each data point to one of the k clusters such that the mean of the data points within each cluster is minimized. This process aims to create clusters where the data points are as similar to each other as possible.

The algorithm begins by randomly selecting k initial cluster centroids. Then, in an iterative fashion, two main steps are performed. First, each data point is assigned to the cluster whose centroid is closest to it, typically measured by Euclidean distance. Second, the centroids of each cluster are recalculated as the mean of all the data points currently assigned to that cluster. These two steps are repeated until the cluster assignments no longer change, or until a predefined maximum number of iterations is reached.

The k-averaging method is widely used due to its simplicity and efficiency, especially for large datasets. However, its performance can be sensitive to the initial choice of centroids, and it may converge to a local optimum rather than the global optimum. The choice of the parameter k, the number of clusters, is also crucial and often requires prior knowledge or experimentation. Variations of the algorithm exist to address some of these limitations, such as k-means++ for improved centroid initialization.