Solved Example Complete Linkage - Agglomerative Hierarchical Clustering Euclidean Dist Mahesh Huddar

Solved Example Complete Linkage - Agglomerative Hierarchical Clustering Euclidean Distance Mahesh Huddar

Problem Definition:
Given a one-dimensional data set {1, 5, 8, 10, 2}, use the agglomerative clustering algorithms with the complete link with Euclidean distance to establish a hierarchical grouping relationship.
By using the cutting threshold of 5, how many clusters are there?
What is their membership in each cluster?

The following concepts are discussed:
______________________________
Solved Example Complete Linkage Clustering -
Agglomerative Hierarchical Clustering Euclidean Distance,
Agglomerative clustering Complete Linkage,
Agglomerative Hierarchical Clustering,
Agglomerative Clustering Euclidean Distance,
Hierarchical Clustering Euclidean Distance,
Hierarchical Clustering with Complete Linkage,
Agglomerative Clustering with Complete Linkage

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[muna]

This is absolutely lovely, thank you so much for this explanation.... 🙏

[nevilmehta]

Really good explanation! Thanks a lot for such an in-depth explanation

[zainellahi]

hi, what if the distance was manhattan distance? So to calculate would it be (5-4) which is 1 which is the same as the euclidean distance

[sahithimudumbai]

Do they mention in the question saying it should be solved using complete linkage or single linkage?

[muna]

Assuming it was average linkage for a dataset {2, 3, 7}... will the average pairwise distance between 2, 3 be:

Square_root((2, 3)^2) / 2 = 0.5