BulDML at Institute of Mathematics and Informatics >
Proceedings >
DiPP >
DiPP 2011 >

Please use this identifier to cite or link to this item:

Title: Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery
Authors: Ivanova, Krassimira
Mitov, Iliya
Stanchev, Peter
Dobreva, Milena
Vanhoof, Koen
Depaire, Benoit
Keywords: Data Mining
Associative Classifier
Metadata Extraction
Cultural Heritage
Issue Date: 2011
Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation: Digital Presentation and Preservation of Cultural and Scientific Heritage, Vol. 1, No 1, (2011), 117p-126p
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 movements.
ISSN: 1314-4006
Appears in Collections:DiPP 2011

Files in This Item:

File Description SizeFormat
13+2011-DiPP-Ivanova_et_al.pdf591.52 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0!   Creative Commons License