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Title: Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery
Authors: IVANOVA, Krassimira 
MITOV, Iliya 
Stanchev, Peter
Dobreva, Milena
DEPAIRE, Benoit 
Issue Date: 2011
Publisher: Institute of Mathematics and Informatics – BAS
Source: Pavlov, Radoslav; Stanchev, Peter (Ed.). Proceedings of the 1st International Conference on Digital Preservation and Presentation of Cultural Heritage, p. 117-126
Abstract: 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 movement.
Keywords: data mining; associative classifier; metadata extraction; cultural heritage
Document URI:
Category: C1
Type: Proceedings Paper
Validations: vabb 2014
Appears in Collections:Research publications

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