7 - Hierarchical Clustering

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What is the purpose of combining rows and columns using Lance-Williams update equations?

To find the position of the minimum distance or maximum similarity.

What is the general form of Lance-Williams update equations?

Several (but not all) linkages can be expressed in this form.

Which linkages cannot be computed with Lance-Williams updates?

MiniMax, Medoid, Hausdorff linkages.

What is the purpose of extracting clusters from a dendrogram?

To obtain the clusters from the hierarchical structure. Signup and view all the answers

Why is it recommended to avoid computing directly in hierarchical clustering?

Direct computation is expensive. Signup and view all the answers

What is the basic idea behind Hierarchical Agglomerative Clustering?

To initially consider every object as a cluster and then iteratively merge the two most similar clusters until only one cluster remains. Signup and view all the answers

What are some variations in Hierarchical Agglomerative Clustering?

Variations include different distance or similarity measures for objects, different distance measures for clusters after merging (Linkage), and various optimizations. Signup and view all the answers

What is Single-linkage in terms of distances of clusters?

Single-linkage considers the minimum distance between clusters as the basis for merging, representing maximum similarity. Signup and view all the answers

Describe the Complete-linkage approach in Hierarchical Agglomerative Clustering.

Complete-linkage involves considering the maximum distance between clusters, indicating minimum similarity, for merging. Signup and view all the answers

What is the concept of Ward-linkage in Hierarchical Agglomerative Clustering?

Ward-linkage focuses on the minimum increase of squared error when merging clusters. Signup and view all the answers

Explain the process of AGNES (Agglomerative Nesting) in clustering.

AGNES involves computing the pairwise distance matrix of objects and using the Lance-Williams equations for merging clusters. Signup and view all the answers

What are some strategies to determine when to stop the hierarchical agglomerative clustering process?

visually inspect the dendrogram, choose interesting branches; stop when clusters remain; stop at a certain distance; significant change in distance; significance via bootstrap resampling; change in cluster sizes or density; constraints satisfied (semi-supervised) Signup and view all the answers

What are some benefits of Hierarchical Agglomerative Clustering (HAC)?

very general: any distance / similarity; easy to understand and interpret; hierarchical result; dendrogram visualization often useful; number of clusters does not need to be fixed beforehand; many variants Signup and view all the answers

What is the main limitation of Hierarchical Agglomerative Clustering (HAC) in terms of scalability?

scalability is the main problem Signup and view all the answers

What is a common issue that users face with Hierarchical Agglomerative Clustering (HAC) in terms of the desired clustering outcome?

in many cases, users want a flat partitioning Signup and view all the answers

What are some challenges or limitations of Hierarchical Agglomerative Clustering (HAC) related to cluster sizes and outliers?

unbalanced cluster sizes; outliers/noise for some linkage strategies Signup and view all the answers

What is one approach to incorporating supervision in Hierarchical Agglomerative Clustering (HAC)?

constraints satisfied (semi-supervised): certain objects are labeled as 'must' or 'should not' be in the same clusters Signup and view all the answers