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Agenda

  • Probability Model-Based Clustering
    • Each object may take a probability to belong to a cluster
  • Clustering High-Dimensional Data
    • Curse of dimensionality: Difficulty of distance measure in high-D space
  • Clustering Graphs and Network Data
    • Similarity measurement and clustering methods for graph and networks
  • Clustering with Constraints
    • Cluster analysis under different kinds of constraints, e.g., that raised from background knowledge or spatial distribution of the objects

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