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Explore the concept of clustering and popular algorithms like Hierarchical and Partitional Clustering. Learn about K-means approach, Self Organizing Maps (SOM), CLICK method, and more with examples and figures.
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Clustering • The process of grouping samples so that the samples are similar within each group.
Algorithm of Clustering • Hierarchical clustering • Organizes the data into larger groups, which contain smaller groups, like a tree or dendrogram. • Algorithms :Agglomerative,Single-linkage, complete-linkage, average-linkage, Ward…. • Partitional clustering • To create one set of clusters that partitions the data into similar groups. • Algorithms: Forgy’s, k-means, Isodata… • SOM,CLICK, CAST, …
Figures of Hierarchical Clustering 1‘ 1 2 3 4 5
Figures of Hierarchical Clustering 2‘ 1 2 3 4 5
Figures of Hierarchical Clustering 2‘ 3‘ 1 2 3 4 5
Figures of Hierarchical Clustering 1 2 3 4 5
K-mean approach • One more input k is required. There are many variants of k-mean. • Sum-of squares criterion • minimize
An example of k-mean approach • Two passes • Begin with k clusters, each consisting of one of the first k samples. For the remaining n-k samples, find the centroid nearest it. After each sample is assigned, re-compute the centroid of the altered cluster. • For each sample, find the centroid nearest it. Put the sample in the cluster identified with this nearest centroid. ( do not need to re-compute.)
CLICK • Use graph theory • Connected component • The edge weight is calculated by statistical probabilities
