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Major Clustering Approaches

  • Partitioning approach:
    • Construct various partitions and then evaluate them by some criterion, e.g., minimizing the sum of square errors
    • Typical methods: k-means, k-medoids, CLARANS
  • Hierarchical approach:
    • Create a hierarchical decomposition of the set of data (or objects) using some criterion
    • Typical methods: Diana, Agnes, BIRCH, CAMELEON
  • Density-based approach:
    • Based on connectivity and density functions
    • Typical methods: DBSACN, OPTICS, DenClue
  • Grid-based approach:
    • based on a multiple-level granularity structure
    • Typical methods: STING, WaveCluster, CLIQUE

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