The k-means algorithm uses the mean points in a given dataset to cluster and discover groups within the dataset. The K is the number of clusters that we want and are hoping to discover. After the k-means algorithm has generated the groupings/clusters, we can pass unknown data to this model to predict which cluster the new data should belong to.
Note that in this kind of algorithm, only the raw uncategorized data is fed to the algorithm without any labels associated with the data. It is up to the algorithm to find out if the data has inherent groups within it.
The k-means algorithm iteratively assigns the data points to the clusters based on the similarities among the features provided. K-means clustering groups the data ...
