Is it possible to use k-means algorithm with just one vector

1. What are the limitations of k-means algorithm?
2. How many iterations does k-means require?
3. What is the requirement of k-means algorithm?
4. Does k-means always give same clusters?

What are the limitations of k-means algorithm?

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.

How many iterations does k-means require?

In this paper (Scalable K-Means by ranked retrieval), the authors stated that K-means converges after 20-50 iterations in all practical situations, even on high dimensional datasets as they tested.

What is the requirement of k-means algorithm?

In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. The 'means' in the K-means refers to averaging of the data; that is, finding the centroid.

Does k-means always give same clusters?

They won't necessarily be the same. Consider observations equally distributed over a circle (radius = 1). Depending on the initial centroids, the algorithm will converge on different solutions.