Comparison of partial orders clustering techniques
In this paper, we compare three approaches of clustering partial ordered subsets of a set of items. First approach was k-medoids clustering algorithm with distance function based on Levenshtein distance. The second approach was k-means algorithm with cosine distance as distance function after vector...
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| Format: | Article |
| Language: | English |
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Ivannikov Institute for System Programming of the Russian Academy of Sciences
2018-10-01
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| Series: | Труды Института системного программирования РАН |
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| Online Access: | https://ispranproceedings.elpub.ru/jour/article/view/829 |
| Summary: | In this paper, we compare three approaches of clustering partial ordered subsets of a set of items. First approach was k-medoids clustering algorithm with distance function based on Levenshtein distance. The second approach was k-means algorithm with cosine distance as distance function after vectorization of partial orders. And the third one was k-medoids algorithm with Kendall's tau as a distance function. We use Adjusted Rand Index as a measure of quality of clustering and find out that clustering with all three methods get stable results when variance of number of items ranked is high. Vectorization of partial orders get best results if number of items ranked is low. |
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| ISSN: | 2079-8156 2220-6426 |