The hybrid clustering algorithm in this paper combines different clustering techniques to group data into smaller sets of prototype vectors. This approach helps reduce computational costs by abstracting the data at the first level, making the second level clustering more efficient. The prototypes created at the first level are less affected by random variations in the data. The researchers tested this algorithm on four datasets and found it to be effective in clustering data efficiently.