Which shape clusters are formed by k-means?
Kmeans assumes spherical shapes of clusters (with radius equal to the distance between the centroid and the furthest data point) and doesn’t work well when clusters are in different shapes such as elliptical clusters.
Which clustering approach can discover clusters of non spherical shape?
In this paper, we propose a genetic clustering algorithm for clustering the data whose clusters are not of spherical shape. It can automatically cluster the data according to the similarities and automatically find the proper number of clusters.
Which is an example of k-means clustering?
Look at Figure 1. Compare the intuitive clusters on the left side with the clusters actually found by k-means on the right side. The comparison shows how k-means can stumble on certain datasets. Figure 1: Ungeneralized k-means example.
Is the k-means procedure valid for globular clusters?
The procedure appears to successfully identify the two expected groupings, however the clusters are clearly not globular. Is this a valid application? I am not sure whether I am violating any assumptions (if there are any?), or whether it is just that k-means often does not work with non-spherical data clusters.
When does k-means do not perform well?
K-means will not perform well when groups are grossly non-spherical. I highly recomend this answer by David Robinson to get a better intuitive understanding of this and the other assumptions of k-means. K-means does not perform well when the groups are grossly non-spherical because k-means will tend to pick spherical groups.
What are the advantages and disadvantages of k-means?
Clustering data of varying sizes and density. 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.