K-Mean Clustering vs Hierarchal Clustering [Machine Learning]

Straight Forward:

K Mean Clustering:

1. In K-Mean clustering we tel machine about how much cluster we want to have.
2. We start with defining with random cluster center and find new cluster center, continuing this same process we'll reach to a stage where our "new cluster centre" and "current cluster centre" will be same. This mean our algorithm is optimise and it is the cluster centre.  

Hierarchal Clustering:

1. In this clustering algorithm of Machine learning we feed data to machine and let machine decide how many cluster it want to group that data.
2. We start with considering very data point as cluster centre and take mean of all nearby data points (depending upon radius you have selected). This process will continue until we optimise our algorithm (using mean shift algorithm) and at the point where we found our convergence is out cluster center.