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An Integration-Based Approach to Pattern Clustering and Classification

  • Laura Sani
  • Gianluca D’Addese
  • Riccardo Pecori
  • Monica Mordonini
  • Marco Villani
  • Stefano Cagnoni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11298)

Abstract

Methods based on information theory, such as the Relevance Index (RI), have been employed to study complex systems for their ability to detect significant groups of variables, well integrated among one another and well separated from the others, which provide a functional block description of the system under analysis. The integration (or zI in its standardized form) is a metric that can express the significance of a group of variables for the system under consideration: the higher the zI, the more significant the group. In this paper, we use this metric for an unusual application to a pattern clustering and classification problem. The results show that the centroids of the clusters of patterns identified by the method are effective for distance-based classification algorithms. We compare such a method with other conventional classification approaches to highlight its main features and to address future research towards the refinement of its accuracy and computational efficiency.

Keywords

Classification Clustering Complex systems RI metrics 

Notes

Acknowledgments

The authors would like to thank Chiara Lasagni for the many tests and for helping us reach full awareness of some of the finer details of the method.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Laura Sani
    • 1
  • Gianluca D’Addese
    • 2
  • Riccardo Pecori
    • 1
    • 3
  • Monica Mordonini
    • 1
  • Marco Villani
    • 2
  • Stefano Cagnoni
    • 1
  1. 1.Department of Engineering and ArchitectureUniversity of ParmaParmaItaly
  2. 2.FIM DepartmentUniversity of Modena and Reggio EmiliaModenaItaly
  3. 3.SMARTEST Research CentreeCAMPUS UniversityNovedrateItaly

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