All the Annoying Assumptions

K-Means clustering method considers two assumptions regarding the clusters -- first that the clusters are spherical and second that the clusters are of similar size. Spherical assumption helps in separating the clusters when the algorithm works on the data and forms clusters. If this assumption is violated, the clusters formed may not be what one expects. On the other hand, assumption over the size of clusters helps in deciding the boundaries of the cluster. Certain resemblance measures (e.g., Euclidean distance)assume that the variables are uncorrelated within clusters.