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Classification is an essential step for architectural historians for better understanding and typology of cultural heritage. This research aims to automatically cluster geometric ornaments of the Ilkhanid period in Iran through using machine learning It examines the application of advanced computer science tools and methods in analysis of architectural heritage by searching the possibility of clustering ornament images with machine learning into different clusters. After examining case studies of mosques, tombs or other buildings in Iran from the Ilkhanid period (1256–1335 CE), 231 images from 36 existing buildings were chosen, and after editing images, an inventory was created based on characterization of each ornament (image) containing: Buildings’ Name, Region, Construction Date, Functionality, Repetition of Motifs, Material, Ornamental Types, Design Complexity, Dominant Colour, Star-Number, Geometric Shape, Geometric Lines, and Geometric Pieces. Next, these images were analysed with small-scale machine learning with the help of the visual programming toolbox Orange (http://orange.biolab.si). The results containing image groups (machine clustered images) were tested with the characterization table of ornaments, and groups of ornaments that represents a style is introduced.

Classification is an essential step for architectural historians for better understanding and typology of cultural heritage. This research aims to automatically cluster geometric ornaments of the Ilkhanid period in Iran through using machine learning It examines the application of advanced computer science tools and methods in analysis of architectural heritage by searching the possibility of clustering ornament images with machine learning into different clusters. After examining case studies of mosques, tombs or other buildings in Iran from the Ilkhanid period (1256–1335 CE), 231 images from 36 existing buildings were chosen, and after editing images, an inventory was created based on characterization of each ornament (image) containing: Buildings' Name, Region, Construction Date, Functionality, Repetition of Motifs, Material, Ornamental Types, Design Complexity, Dominant Colour, Star-Number, Geometric Shape, Geometric Lines, and Geometric Pieces. Next, these images were analysed with small-scale machine learning with the help of the visual programming toolbox Orange (http://orange.biolab.si). The results containing image groups (machine clustered images) were tested with the characterization table of ornaments, and groups of ornaments that represents a style is introduced. [ABSTRACT FROM AUTHOR]