**January 16, 2021**

Following are the difference between K-Means and Hierarchical Clustering Algorithm (HCA)

- K-Means is that it needs us to pre-enter the number of clusters (K) but Hierarchical clustering has no such requirements. The algorithm on itself deduces the optimum number of cluster and displays it form of dendrogram.
- Performance of K-Means on spherical data is better than that of HCA
- Hierarchical clustering is a purely agglomerative approach and goes on to build one giant cluster. K-Means algorithm in all its iterations has same number of clusters.
- K-Means need circular data, while Hierarchical clustering has no such requirement.
- K-Means uses median or mean to compute centroid for representing cluster while HCA has various linkage method that may or may not employ the centroid.
- With introduction of mini batches K-Means can work with very large datasets but HCA lacks in this regard.
- Hierarchical methods are suited for cases which require arrangement of the clusters into a natural hierarchy. In K-means all clusters are on same level i.e. similar WCSS or cohesiveness.
- HCA can produce reproducible results while older versions of K-Means can’t
- K-Means simply divides data into mutually exclusive subsets while HCA arranges it into a tree format.

by : Monis Khan

**Quick Summary**:

Following are the difference between K-Means and Hierarchical Clustering Algorithm (HCA) K-Means is that it needs us to pre-enter the number of clusters (K) but Hierarchical clustering has no such requirements. The algorithm on itself deduces the optimum number of cluster and displays it form of dendrogram. Performance of K-Means on spherical data is better […]