Using a variable weighting κ-means method to build a decision cluster classification model

Yan Li, Edward Hung, Fu Lai Korris Chung, Joshua Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


In this paper, a new classification method (ADCC) for high-dimensional data is proposed. In this method, a decision cluster classification (DCC) model consists of a set of disjoint decision clusters, each labeled with a dominant class that determines the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a variable weighting κ-means algorithm. Then, the DCC model is extracted from the tree. Various tests including AndersonDarling test are used to determine the stopping condition of the tree growing. A series of experiments on both synthetic and real data sets have been conducted. Their results show that the new classification method (ADCC) performed better in accuracy and scalability than existing methods like κ-NN, decision tree and SVM. ADCC is particularly suitable for large, high-dimensional data with many classes.
Original languageEnglish
Article number1250003
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number2
Publication statusPublished - 1 Mar 2012


  • κ-NN
  • classification
  • Clustering
  • W-κ-means

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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