Hierarchical Clustering a Produces a set of nested clusters organized as a hierarchical tree a Can be visualized as a dendrogram o a tree-like diagram that records the sequences of merges or splIts ●4 0.15 0.1
Hierarchical Clustering ◼ Produces a set of nested clusters organized as a hierarchical tree. ◼ Can be visualized as a dendrogram ◆ A tree-like diagram that records the sequences of merges or splits 1 3 2 5 4 6 0 0.05 0.1 0.15 0.2 1 2 3 4 5 6 1 2 3 4 5 12
Strengths of Hierarchical Clustering No assumptions on the number of clusters Any desired number of clusters can be obtained by 'cutting the dendogram at the proper level a Hierarchical clustering may correspond to meaningful taxonomies Example in biological sciences(e. g, phylogeny reconstruction, etc), web(e.g, product catalogs etc
Strengths of Hierarchical Clustering ◼ No assumptions on the number of clusters ◆ Any desired number of clusters can be obtained by ‘cutting’ the dendogram at the proper level ◼ Hierarchical clustering may correspond to meaningful taxonomies ◆ Example in biological sciences (e.g., phylogeny reconstruction, etc), web (e.g., product catalogs) etc. 13
Hierarchical Clustering Two main types of hierarchical clustering ◆ Agglomerative a Start with the points as individual clusters a At each step, merge the closest pair of clusters until only one cluster (or k clusters)left ◆ Divisive: a Start with one, all-inclusive cluster o At each step, split a cluster until each cluster contains a point (or there are k clusters a Traditional hierarchical algorithms use a similarity or distance matrix Merge or split one cluster at a time
Hierarchical Clustering ◼ Two main types of hierarchical clustering ◆ Agglomerative: Start with the points as individual clusters At each step, merge the closest pair of clusters until only one cluster (or k clusters) left ◆ Divisive: Start with one, all-inclusive cluster At each step, split a cluster until each cluster contains a point (or there are k clusters) ◼ Traditional hierarchical algorithms use a similarity or distance matrix ◆ Merge or split one cluster at a time 14
Hierarchical Clustering: two types Step StepStep Step Step 011234 agglomerative a b a bcde C cde d e e divisive StepStepStep Step Step 2 0
15 Step 0 Step 1 Step 2 Step 3 Step 4 b d c e a a b d e c d e a b c d e Step 4 Step 3 Step 2 Step 1 Step 0 agglomerative divisive Hierarchical Clustering: two types
Practice? a b bc de b d def c e Raw data bcdef abcdef Traditional representati on
16 Practice?