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CrossClus: An Overview

  • Measure similarity between features by how they group objects into clusters
  • Use a heuristic method to search for pertinent features
    • Start from user-specified feature and gradually expand search range
  • Use tuple ID propagation to create feature values
    • Features can be easily created during the expansion of search range, by propagating IDs
  • Explore three clustering algorithms: k-means, k-medoids, and hierarchical clustering

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