Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures
Automatic grouping (clustering) involves dividing a set of objects into subsets (groups) so that the objects from one subset are more similar to each other than to the objects from other subsets according to some criterion. Kohonen neural networks are a class of artificial neural networks, the main...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/6/191 |
| _version_ | 1827663118572978176 |
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| author | Guzel Shkaberina Leonid Verenev Elena Tovbis Natalia Rezova Lev Kazakovtsev |
| author_facet | Guzel Shkaberina Leonid Verenev Elena Tovbis Natalia Rezova Lev Kazakovtsev |
| author_sort | Guzel Shkaberina |
| collection | DOAJ |
| description | Automatic grouping (clustering) involves dividing a set of objects into subsets (groups) so that the objects from one subset are more similar to each other than to the objects from other subsets according to some criterion. Kohonen neural networks are a class of artificial neural networks, the main element of which is a layer of adaptive linear adders, operating on the principle of “winner takes all”. One of the advantages of Kohonen networks is their ability of online clustering. Greedy agglomerative procedures in clustering consistently improve the result in some neighborhood of a known solution, choosing as the next solution the option that provides the least increase in the objective function. Algorithms using the agglomerative greedy heuristics demonstrate precise and stable results for a k-means model. In our study, we propose a greedy agglomerative heuristic algorithm based on a Kohonen neural network with distance measure variations to cluster industrial products. Computational experiments demonstrate the comparative efficiency and accuracy of using the greedy agglomerative heuristic in the problem of grouping of industrial products into homogeneous production batches. |
| first_indexed | 2024-03-10T00:38:23Z |
| format | Article |
| id | doaj.art-74f7e9e9a2f44a398e62363fbe1bef43 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T00:38:23Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-74f7e9e9a2f44a398e62363fbe1bef432023-11-23T15:13:04ZengMDPI AGAlgorithms1999-48932022-06-0115619110.3390/a15060191Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance MeasuresGuzel Shkaberina0Leonid Verenev1Elena Tovbis2Natalia Rezova3Lev Kazakovtsev4Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Av., 660037 Krasnoyarsk, RussiaInstitute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Av., 660037 Krasnoyarsk, RussiaInstitute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Av., 660037 Krasnoyarsk, RussiaInstitute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Av., 660037 Krasnoyarsk, RussiaInstitute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Av., 660037 Krasnoyarsk, RussiaAutomatic grouping (clustering) involves dividing a set of objects into subsets (groups) so that the objects from one subset are more similar to each other than to the objects from other subsets according to some criterion. Kohonen neural networks are a class of artificial neural networks, the main element of which is a layer of adaptive linear adders, operating on the principle of “winner takes all”. One of the advantages of Kohonen networks is their ability of online clustering. Greedy agglomerative procedures in clustering consistently improve the result in some neighborhood of a known solution, choosing as the next solution the option that provides the least increase in the objective function. Algorithms using the agglomerative greedy heuristics demonstrate precise and stable results for a k-means model. In our study, we propose a greedy agglomerative heuristic algorithm based on a Kohonen neural network with distance measure variations to cluster industrial products. Computational experiments demonstrate the comparative efficiency and accuracy of using the greedy agglomerative heuristic in the problem of grouping of industrial products into homogeneous production batches.https://www.mdpi.com/1999-4893/15/6/191clusteringgreedy agglomerative heuristicKohonen neural networkself-organized Kohonen map |
| spellingShingle | Guzel Shkaberina Leonid Verenev Elena Tovbis Natalia Rezova Lev Kazakovtsev Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures Algorithms clustering greedy agglomerative heuristic Kohonen neural network self-organized Kohonen map |
| title | Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures |
| title_full | Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures |
| title_fullStr | Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures |
| title_full_unstemmed | Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures |
| title_short | Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures |
| title_sort | clustering algorithm with a greedy agglomerative heuristic and special distance measures |
| topic | clustering greedy agglomerative heuristic Kohonen neural network self-organized Kohonen map |
| url | https://www.mdpi.com/1999-4893/15/6/191 |
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