Abstract
The purpose of this work is to develop the structure of the algorithm for automatic grouping (clustering) of 3D model metadata which may include model name, dimensions, file size, file format, keywords etc.. The relevance of this work is determined by the need for companies to process complexly structured data, in particular 3D models, and detect groups of similar 3D models for forming their catalogues and other purposes. "similarity", we mean the proximity of objects in a multidimensional space of features, and the problem is reduced to partitioning this space into subspaces of objects so that the objects located in the subspaces form homogeneous groups. We propose an algorithm for automatic grouping of the 3D models based on their metadata, which enables us to group objects according to their numeric and categorical characteristics. The experiments were carried out with the involvement of experts, their evaluation showed the high efficiency of the developed method.
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Kazakovtsev, L. A., Kutsevalova, V. V., & Kazakovtsev, V. L. (2023). System for Automatic Grouping of Metadata of Three-Dimensional Models. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 343-350). European Publisher. https://doi.org/10.15405/epct.23021.42