A robust adaptive clustering analysis method for automatic identification of clusters

Pik Yin Mok, H. Q. Huang, Y. L. Kwok, Sau Chuen Joe Au

Research output: Journal article publicationJournal articleAcademic researchpeer-review

75 Citations (Scopus)


Identifying the optimal cluster number and generating reliable clustering results are necessary but challenging tasks in cluster analysis. The effectiveness of clustering analysis relies not only on the assumption of cluster number but also on the clustering algorithm employed. This paper proposes a new clustering analysis method that identifies the desired cluster number and produces, at the same time, reliable clustering solutions. It first obtains many clustering results from a specific algorithm, such as Fuzzy C-Means (FCM), and then integrates these different results as a judgement matrix. An iterative graph-partitioning process is implemented to identify the desired cluster number and the final result. The proposed method is a robust approach as it is demonstrated its effectiveness in clustering 2D data sets and multi-dimensional real-world data sets of different shapes. The method is compared with cluster validity analysis and other methods such as spectral clustering and cluster ensemble methods. The method is also shown efficient in mesh segmentation applications. The proposed method is also adaptive because it not only works with the FCM algorithm but also other clustering methods like the k-means algorithm.
Original languageEnglish
Pages (from-to)3017-3033
Number of pages17
JournalPattern Recognition
Issue number8
Publication statusPublished - 1 Aug 2012


  • Cluster analysis
  • Cluster ensembles
  • Cluster validity
  • Fuzzy C-Means
  • Fuzzy clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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