The correct answer is: number of clusters.
K-means is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Intuitively, each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The number of clusters is a parameter of the K-means algorithm. It is a fixed value that must be specified before the algorithm can be run. The number of clusters should be chosen so that it is large enough to capture the underlying structure of the data, but not so large that the clusters become too small and noisy.
The other options are incorrect.
- Complexity is not a term that is typically used in the context of K-means clustering.
- No of iterations is not a parameter of the K-means algorithm. The number of iterations is determined by the algorithm itself.
- Fixed value is not a term that is typically used in the context of K-means clustering.