Discover the semantic topology in high-dimensional data

I. Jen Chiang

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Discovering the homogeneous concept groups in the high-dimensional data sets and clustering them accordingly are contemporary challenge. Conventional clustering techniques often based on Euclidean metric. However, the metric is ad hoc not intrinsic to the semantic of the documents. In this paper, we are proposing a novel approach, in which the semantic space of high-dimensional data is structured as a simplicial complex of Euclidean space (a hypergraph but with different focus). Such a simplicial structure intrinsically captures the semantic of the data; for example, the coherent topics of documents will appear in the same connected component. Finally, we cluster the data by the structure of concepts, which is organized by such a geometry.

Original languageEnglish
Pages (from-to)256-262
Number of pages7
JournalExpert Systems with Applications
Issue number1
Publication statusPublished - Jul 2007


  • Association rules
  • Document clustering
  • Hierarchical clustering
  • Simplicial complex

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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