On a New Evidential C-Means Algorithm with Instance-Level Constraints

  • Jiarui XieEmail author
  • Violaine Antoine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11940)


Clustering is an unsupervised task whose performances can be highly improved with background knowledge. As a consequence, several semi-supervised clustering approaches have proposed to integrate prior information in the form of constraints, generally at the instance-level. Amongst them, evidential semi-supervised clustering algorithms, such as CECM or SECM algorithm, rely on the theoretical foundation of belief function which extends the probabilistic theory and allows us to express many types of uncertainty about the assignment of an object to a cluster. In this framework, no evidential clustering algorithm has ever mixed different types of instance-level constraints. We propose here to combine pairwise constraints and labeled data constraints in order to better retrieve information from the background knowledge. The new algorithm, called LPECM, shows good performances on synthetic and real data sets.


Labeled data constraints Pairwise constraints Instance-level constraints Belief function Evidential clustering Semi-supervised clustering 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Clermont Auvergne University, UMR 6158 CNRS, LIMOSClermont-FerrandFrance
  2. 2.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinPeople’s Republic of China

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