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  • Partition data set into clusters based on similarity, and store cluster representation (e.g., centroid and diameter) only
  • Can be very effective if data is clustered but not if data is “smeared”
  • Can have hierarchical clustering and be stored in multi-dimensional index tree structures
  • There are many choices of clustering definitions and clustering algorithms
  • Cluster analysis will be studied in depth in Chapter 10

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