Divisive Clustering Approach

• It is one of the types of hierarchical clustering.
• In theory, it can be done by initially grouping all the observations into one cluster, and then successively splitting these clusters. This is known as divisive hierarchical clustering.
• This approach is also known as the Top-Down Approach.
• Initially, all the points in the dataset belong to one cluster, and a split is performed recursively as one moves down the hierarchy.
• But, Divisive clustering is rarely done in practice.

Steps of Divisive Clustering Approach -

• This approach starts with all of the objects in the same cluster.
• In the continuous iteration, a cluster is split up into smaller clusters.
• It is down until each object in one cluster or the termination condition holds.
• This method is rigid, i.e., once a merging or splitting is done, it can never be undone.

Features of Divisive Clustering Approach -

• Divisive clustering is more complex as compared to agglomerative clustering, as in the case of divisive clustering we need a flat clustering method as a “subroutine” to split each cluster until we have each data having its singleton cluster.
• Divisive clustering is more efficient if we do not generate a complete hierarchy down to individual data leaves.
• There is evidence that divisive algorithms produce more accurate hierarchies than bottom-up algorithms in some circumstances.
• Divisive clustering given a fixed number of top levels, using an efficient flat algorithm like K-Means, divisive algorithms are linear in the number of patterns and clusters.
• Divisive algorithm is also more accurate.
• Because divisive clustering takes into consideration the global distribution of data when making top-level partitioning decisions.