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Clustering The process of grouping samples so that the samples are similar within each group.
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Clustering
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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, …
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Figures of Hierarchical Clustering
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Figures of Hierarchical Clustering
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Figures of Hierarchical Clustering
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Figures of Hierarchical Clustering
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Hierarchical Clustering
Method Distance metric Single-link Average-link Complete-link Centriod
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K-mean approach One more input k is required. There are many variants of k-mean. Sum-of squares criterion minimize
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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.)
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Self Organizing Maps
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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Examples
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CLICK Use graph theory Connected component The edge weight is calculated by statistical probabilities
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