POCS-based Clustering Algorithm

Le Anh Tran, Henock M. Deberneh, Truong Dong Do, Thanh Dat Nguyen, My Ha Le, Dong Chul Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-Means clustering algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - IWIS 2022
Subtitle of host publication2nd International Workshop on Intelligent Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350398939
StatePublished - Aug 2022
Event2nd International Workshop on Intelligent Systems, IWIS 2022 - Ulsan, Korea, Republic of
Duration: Aug 17 2022Aug 19 2022

Publication series

NameProceedings - IWIS 2022: 2nd International Workshop on Intelligent Systems


Conference2nd International Workshop on Intelligent Systems, IWIS 2022
Country/TerritoryKorea, Republic of


  • K-Means
  • POCS
  • clustering
  • machine learning
  • unsupervised learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems


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