Suppose you run K-means clustering algorithm on a given dataset. What are the factors on which the final clusters depend on ? I. The value of K II. The initial cluster seeds chosen III. The distance function used.Suppose you run K-means clustering algorithm on a given dataset. What are the factors on which the final clusters depend on ? I. The value of K II. The initial cluster seeds chosen III. The distance function used.
Answers
D) I, II and III
The final clusters depend on "the value of K", "the initial cluster seeds chosen" and "the distance function used".
In the K-means algorithm the K data elements are chosen as the initial centers at the very first. Then using the Euclidian distance formula the distances of all data elements are calculated. The quality of the final clustering and the time complexity of the K-means algorithm depend on the random selection of the initial centroids. The basic key step of K-means algorithm is to choose the proper initial centroids, because the initial centroids are chosen randomly and therefore for the same input data different clusters are obtained for different runs. Also, if the initial centers are not chosen carefully, the computation will run the chance of converging to a local minimum rather than the global minimum solution.
Hence all the given options are correct.