Question

What is the overall complexity of the the Agglomerative Hierarchical Clustering ?

Answer 1

In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram. The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of and requires memory, which makes it too slow for even medium data sets.

Answer 2

Explanation:

• Agglomerative clustering algorithms, in the beginning, view each data as a 'single cluster' and then 'agglomerate' pairs of 'clusters' successively until all clusters are jointed into a single cluster having all data.
• The time complexity of a naive 'agglomerative cluster' is O(n3) because in each of the N-1 iterations we scan the 'N x N matrix' dist mat exhaustively for the lowest distance. Using priority queue data structure we can reduce this complexity to $O(n^{2}logn)$. By using 'some more optimizations' it can be 'brought down to $O(n^{2})$'.

To know more

Which is one of the hierarchical clustering approaches?

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_____ is frequently referred to as k-means clustering. agglomerative clustering divisive clustering non-hierarchical clustering optimization partitioning

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