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Quick Look At Clustering
 

Quick Look At Clustering

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Quick Look At Clustering

Quick Look At Clustering

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    Quick Look At Clustering Quick Look At Clustering Presentation Transcript

    • CLUSTERING
    • Introduction
      Centroid - Center of a cluster
      Centroid could be either a real point or an imaginary one.
      Objective function –
      Measures the quality of clustering (small value is desirable)
      Calculated by summing the squares of distances of each point from the centroid of the cluster
      Two types of Clustering are:
      k-means Clustering
      Hierarchical Clustering
    • k-means Clustering
      It is an exclusive clustering algorithm
      Algorithm:
      Select a value for ‘k’
      Select ‘k’ objects in an arbitrary fashion. Use it as an initial set of k centroids
      Assign each object to the cluster for which it is nearest to the centroid
      Recalculate the centroids
      Repeat steps 3 & 4 until centroids don’t move.
      It may not find the best set of clusters but will always terminate.
    • Agglomerative Hierarchical Clustering
      Algorithm:
      Assign each object to its own single-object cluster. Calculate the distance between each pair (distance matrix)
      Select and merge the closest pairs
      Calculate the distance between this new cluster and other clusters.
      Repeat steps 2 & 3 until all objects are in single cluster
    • Example
      Before clustering
      After two passes
    • Example
      It gives the entire hierarchy of clusters
      Dendrogram (A Binary tree) – End result of hierarchical clustering
    • Distance Measure
      Three ways of calculating distances:
      Single-link clustering
      Shortest distance from any member of one cluster to any member of another cluster
      Complete-link clustering
      Longest distance from any member of one cluster to any member of another cluster
      Average-link clustering
      Average distance from any member of one cluster to any member of another cluster
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