No proper kmeans clustering of images in matlab

1. What is the problem with k-means clustering?
2. Can Kmeans be used for image classification?
3. How do you overcome limitations of k-means clustering?

What is the problem with k-means clustering?

k-means has trouble clustering data where clusters are of varying sizes and density. To cluster such data, you need to generalize k-means as described in the Advantages section. Clustering outliers. Centroids can be dragged by outliers, or outliers might get their own cluster instead of being ignored.

Can Kmeans be used for image classification?

Yes! K-Means Clustering can be used for Image Classification of MNIST dataset. Here's how. K-means clustering is an unsupervised learning algorithm which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest centroid.

How do you overcome limitations of k-means clustering?

One way to overcome those weaknesses is to use K-mean clustering only if there are available many data. To overcome outliers problem, we can use median instead of mean. Some people pointed out that K means clustering cannot be used for other type of data rather than quantitative data.