Estimation of the Features Influence on Cluster Partition

Daria Kolesnikova, Yuri Andreev, Radda Iureva

2021

Abstract

The use of machine learning and clustering tools for production management, operational and strategic planning is an urgent task. Industrial automation and Industry 4.0 in general stimulate the use of new technologies. So, for the analytics of many business processes and tasks, it is possible to use clustering. This paper evaluates the clustering performance for supplier evaluation considering the influence of preference features. Clustering is mostly unsupervised procedure, and most clustering algorithm depend on some certain assumptions. Subgroups present in the dataset are formed on the base of these assumptions. Consequently, in most cases, the resulting cluster groups require validation and reliability assessment.

Paper Citation

in Harvard Style

Kolesnikova D., Andreev Y. and Iureva R. (2021). Estimation of the Features Influence on Cluster Partition. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-522-7, pages 739-744. DOI: 10.5220/0010545907390744

in Bibtex Style

@conference{icinco21,
author={Daria Kolesnikova and Yuri Andreev and Radda Iureva},
title={Estimation of the Features Influence on Cluster Partition},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2021},
pages={739-744},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010545907390744},
isbn={978-989-758-522-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Estimation of the Features Influence on Cluster Partition
SN - 978-989-758-522-7
AU - Kolesnikova D.
AU - Andreev Y.
AU - Iureva R.
PY - 2021
SP - 739
EP - 744
DO - 10.5220/0010545907390744