Kmeans++ matlab

K-means is a clustering algorithm in Matlab that partitions a given dataset into k clusters. The algorithm works by randomly selecting k initial centroids and assigning each data point to the nearest centroid. Then, the centroids are updated by computing the mean of all data points assigned to each cluster. This process is repeated until the centroids no longer change or a maximum number of iterations is reached.

To use the K-means algorithm in Matlab, the following syntax can be used:

[idx, C] = kmeans(X, k)

where X is the dataset, k is the number of clusters, idx is a vector of indices that specifies the cluster to which each data point belongs, and C is a matrix of final centroid locations.

Additionally, Matlab provides various options to customize the K-means algorithm such as specifying initial centroids, setting a maximum number of iterations, and selecting different distance metrics.