The K-means clustering and Hierarchical Clustering both are the machine learning algorithms. Below are some main differences between both the clustering:
| K-means clustering | Hierarchal Clustering |
|---|---|
| K-means clustering is a simple clustering algorithm in which objects are divided into clusters. | Hierarchal clustering shows the hierarchal or parent-child relationship between the clusters. |
| In k-means clustering, we need prior knowledge of k to define the number of clusters which sometimes may be difficult. | In hierarchal clustering, we don’t need prior knowledge of the number of clusters, and we can choose as per our requirement. |
| K-means clustering can handle big data better than hierarchal clustering. | Hierarchal clustering cannot handle big data in a better way. |
| Time complexity of K-means is O(n) (Linear). | Time complexity of hierarchal clustering is O(n2)(Clustering). |
