Скачать или смотреть K-Means Clustering Algorithm In Machine Learning| Clustering Algorithm Example (

Unsupervised Learning:
#K-#means is an #unsupervised algorithm, meaning it doesn't require labeled data. It identifies patterns and structures within the data itself.
Clustering:
K-means #groups data #points based on similarity. Data points within a cluster are more similar to each other than to data points in other clusters.
Centroids:
K-means uses centroids to represent the "center" of each cluster. These centroids are the means of the points assigned to that cluster.
Iterative Process:
The algorithm works iteratively. It assigns data points to the nearest centroid, then recalculates the centroid positions based on the new assignments. This process repeats until the cluster assignments no longer change significantly.
Distance Metric:
K-means typically uses Euclidean distance to measure the distance between data points and centroids, but other distance metrics can also(‪@ECL365CLASSES‬
k-mean
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