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Authors: Daria Kolesnikova ; Yuri Andreev and Radda Iureva

Affiliation: ITMO University, Saint Petersburg, Russian Federation

Keyword(s): Clustering, Machine Learning, Dataset, Preference, Production Planning.

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.

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Paper citation in several formats:
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 - ICINCO; ISBN 978-989-758-522-7; ISSN 2184-2809, SciTePress, pages 739-744. DOI: 10.5220/0010545907390744

@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 - ICINCO},
year={2021},
pages={739-744},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010545907390744},
isbn={978-989-758-522-7},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO
TI - Estimation of the Features Influence on Cluster Partition
SN - 978-989-758-522-7
IS - 2184-2809
AU - Kolesnikova, D.
AU - Andreev, Y.
AU - Iureva, R.
PY - 2021
SP - 739
EP - 744
DO - 10.5220/0010545907390744
PB - SciTePress

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