Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery


  • Krassimira Ivanova Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
  • Iliya Mitov Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
  • Peter L. Stanchev Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria; Kettering University, Flint, USA



Data Mining, Associative Classifier, Metadata Extraction, Cultural Heritage


Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common approach and focuses on confidence first by retaining only 100% confidence rules. The classification tasks in the field of cultural heritage usually deal with data sets with many class labels. This variety is caused by the richness of accumulated culture during the centuries. Comparisons of classifier PGN with other classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in recognizing multi-class datasets on collections of masterpieces from different West and East European Fine Art authors and movements.


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How to Cite

Ivanova, K., Mitov, I., & L. Stanchev, P. (2011). Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery. Digital Presentation and Preservation of Cultural and Scientific Heritage, 1, 117–126.

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