**January 16, 2021**

Following are the challenges faced by K-Means Clustering:

- k-Means doesn’t perform well if the clusters have varying sizes, different densities, or non-spherical shapes.
- Has to be run for a certain amount of iteration or it would produce a suboptimal result.
- Computationally expensive as distance is to be calculated from each centroid to all data points.
- Can’t handle high dimensional data
- Number of clusters need to be specified
- Since initial centroids are arbitrarily chosen, the result is not exactly replicable.
- Shows out of memory error for large datasets

by : Monis Khan

**Quick Summary**:

Following are the challenges faced by K-Means Clustering: k-Means doesn’t perform well if the clusters have varying sizes, different densities, or non-spherical shapes. Has to be run for a certain amount of iteration or it would produce a suboptimal result. Computationally expensive as distance is to be calculated from each centroid to all data points. […]