Data Analytics (CS40003) Dr. Debasis Samanta Associate Professor


Compute the “cluster centers” of each cluster. These become the new cluster centroids. Repeat step 2-3 until the convergence criterion is satisfied Stop



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13ClusteringTechniques

  • Compute the “cluster centers” of each cluster. These become the new cluster centroids.
  • Repeat step 2-3 until the convergence criterion is satisfied
  • Stop
  • k-Means Algorithm

    Note:

    • Objects are defined in terms of set of attributes. where each is continuous data type.
    • Distance computation: Any distance such as or cosine similarity.
    • Minimum distance is the measure of closeness between an object and centroid.
    • Mean Calculation: It is the mean value of each attribute values of all objects.
    • Convergence criteria: Any one of the following are termination condition of the algorithm.
      • Number of maximum iteration permissible.
      • No change of centroid values in any cluster.
      • Zero (or no significant) movement(s) of object from one cluster to another.
      • Cluster quality reaches to a certain level of acceptance.
    •  

    Illustration of k-Means clustering algorithms

    Table 16.1: 16 objects with two attributes and .

    •  

    A1

    A2

    6.8

    12.6

    0.8

    9.8

    1.2

    11.6

    2.8

    9.6

    3.8

    9.9

    4.4

    6.5

    4.8

    1.1

    6.0

    19.9

    6.2

    18.5

    7.6


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