Ans :
Hierarchical Clustering analysis is an algorithm that groups the data points with similar properties and these groups are termed “clusters”. As a result of hierarchical clustering, we get a set of clusters, and these clusters are always different from each other. Clustering of this data into clusters is classified as:
Agglomerative Clustering (involving decomposition of cluster using bottom-up strategy)
Divisive Clustering (involving decomposition of cluster using top-down strategy)
Hierarchical clustering Technique in terms of space and time complexity:
Space complexity: When the number of data points is large, the space required for the Hierarchical Clustering Technique is large since the similarity matrix must be stored in RAM. The space complexity ismeasured by the order of the square of n.
Space complexity = O(n²) where n is the number of data points.
Time complexity: The time complexity is also very high because we have to execute n iterations and update and restore the similarity matrix in each iteration. The order of the cube of n is the time complexity.
Time complexity = O(n³) where n is the number of data points.
