<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for processing such data are developed based on the well-known k-means procedure. Relations to factor analysis are shown. The problems of improving clustering quality and of finding the proper number of clusters in the case of pairwise comparisons are considered. Illustrative examples are provided.</p>
</abstract>

In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for processing such data are developed based on the well-known k-means procedure. Relations to factor analysis are shown. The problems of improving clustering quality and of finding the proper number of clusters in the case of pairwise comparisons are considered. Illustrative examples are provided.

Clustering of Data Represented by Pairwise Comparisons

Control and Cybernetics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Clustering of Data Represented by Pairwise Comparisons"}

In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for...

Clustering of Data Represented by Pairwise Comparisons

Data publikacji: 22 mar 2023

Zakres stron: 343 - 387

Otrzymano: 01 sie 2022

Przyjęty: 01 wrz 2022

DOI: https://doi.org/10.2478/candc-2022-0021

Słowa kluczowe
clustering, -means, distance, similarity

© 2022 Sergey Dvoenko, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Clustering of Data Represented by Pairwise Comparisons

Data publikacji: 22 mar 2023

Zakres stron: 343 - 387

Otrzymano: 01 sie 2022

Przyjęty: 01 wrz 2022

DOI: https://doi.org/10.2478/candc-2022-0021

Słowa kluczoweclustering, -means, distance, similarity

© 2022 Sergey Dvoenko, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
clustering, -means, distance, similarity