Determining the Optimal Number of Clusters by an Extended RPCL Algorithm

Mn Li, Man Wai Mak, Chi Kwong Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


Determining an appropriate number of clusters is a difficult yet important problem that the rival penalized competitive learning (RPCL) algorithm was designed to solve, but its performance is not satifactory with overlapping clusters or cases where input vectors contain dependent components. VVe address this problem by incorporating full covariance matrices into the original RPCL algorithm. The resulting extended RPCL algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is used to determine the number ofclusters in a Gaussian distribution. It is also used to optimize the architecture of etliptical basis function networks for speaker verification and vowel classification. VVe found that covariance matrices obtained by the extended RPCL algorithm have a better representation of clusters than those obtained by the original RPCL algorithm, resulting in a lower verification error rate in speaker verification and a higher recognition accuracy in vowel classifieation.

Original languageEnglish
Pages (from-to)467-473
Number of pages7
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Issue number6
Publication statusPublished - Dec 1999


  • Clustering
  • Competitive learning
  • Elliptical basis function networks
  • RPCL algorithm
  • Speaker verification

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Determining the Optimal Number of Clusters by an Extended RPCL Algorithm'. Together they form a unique fingerprint.

Cite this