Some important limitations of K-means are that it is sensitive to outliers, it requires the specification of the number of clusters, and it tend to produce clusters that have globular shapes.
Also K-means is not suitable for clustering data with non-numeric features or categorical data, as it relies on the mean of the data points in order to determine the clusters.
Finally, K-means can fail to converge on the optimal clustering solution if the initial cluster centers are chosen poorly.
Also K-means is not suitable for clustering data with non-numeric features or categorical data, as it relies on the mean of the data points in order to determine the clusters.
Finally, K-means can fail to converge on the optimal clustering solution if the initial cluster centers are chosen poorly.
Statistics: Posted by admin — Tue Jan 31, 2023 1:10 am
