Cross-Entropy Clustering Approach to One-Class Classification

  • Przemysaw Spurek
  • Mateusz Wójcik
  • Jacek Tabor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9119)


Cross-entropy clustering (CEC) is a density model based clustering algorithm. In this paper we apply CEC to the one-class classification, which has several advantages over classical approaches based on Expectation Maximization (EM) and Support Vector Machines (SVM). More precisely, our model allows the use of various types of gaussian models with low computational complexity. We test the designed method on real data coming from the monitoring systems of wind turbines.


Covariance matrixa Gaussian filter Mathematical morphology Electron microscopy 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Przemysaw Spurek
    • 1
  • Mateusz Wójcik
    • 2
  • Jacek Tabor
    • 1
  1. 1.Faculty of Mathematics and Computer ScienceJagiellonian UniversityKrakówPoland
  2. 2.Faculty of Electrotechnics, Automation, Computer Science and Biomedical EngineeringAGH University of Science and TechnologyKrakówPoland

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