Hierarchical unsupervised fuzzy clustering

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


A new recursive algorithm for hierarchical fuzzy partitioning is presented. The algorithm has the advantages of hierarchical clustering, while maintaining fuzzy clustering rules. Each pattern can have a nonzero membership in more than one subset of the data in the hierarchy. Optimal feature extraction and reduction is optionally reapplied for each subset. Combining hierarchical and fuzzy concepts is suggested as a natural feasible solution to the cluster validity problem of real data. The convergence and membership conservation of the algorithm are proven. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class.

Original languageEnglish
Pages (from-to)723-733
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Issue number6
StatePublished - 1 Dec 1999


  • Cluster validity
  • Hierarchical clustering
  • Hybrid systems
  • Pattern recognition
  • Projection pursuit
  • Recursive feature extraction
  • Unsupervised fuzzy clustering

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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