The divisive clustering algorithm is a top-down clustering approach in which all points in the dataset are initially assigned to one cluster and then split iteratively as one progresses down the hierarchy.
Agglomerative The hierarchical clustering method allows clusters to be read from bottom to top, and the algorithm always reads from the sub-component first before moving to the parent. Divisive, on the other hand, employs a top-down method in which the parent is visited first, followed by the child.
Agglomerative clustering is done from the bottom up, with each data point starting in its own cluster. These clusters are then greedily joined by merging the two clusters that are the most similar. Divisive clustering works from the top down, with all data points starting in the same cluster.
A divide-and-conquer algorithm is also more precise. Without first examining the global distribution of data, agglomerative clustering makes judgments based on local patterns or neighbour points. These early decisions are irreversible.