Research of nonparametric density estimation algorithms by applying clustering methods
One of the ways to improve the accuracy of probability density estimation is multi-mode density treating as the mixture of single-mode one. In this paper we offer to use data clustering in the first place and to estimate density in every cluster separately. To objectively compare the performance, M...
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| Format: | Article |
| Language: | English |
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Vilnius University Press
2023-09-01
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| Series: | Lietuvos Matematikos Rinkinys |
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| Online Access: | https://www.zurnalai.vu.lt/LMR/article/view/30726 |
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| _version_ | 1825197217664729088 |
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| author | Rasa Šmidtaitė Tomas Ruzgas |
| author_facet | Rasa Šmidtaitė Tomas Ruzgas |
| author_sort | Rasa Šmidtaitė |
| collection | DOAJ |
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One of the ways to improve the accuracy of probability density estimation is multi-mode density treating as the mixture of single-mode one. In this paper we offer to use data clustering in the first place and to estimate density in every cluster separately. To objectively compare the performance, Monte Carlo approximation is used. While using various methods to evaluate the accuracy of probability density estimations we tried to use clustered and not clustered data. In this paper we also tried to reveal the usefulness of using clustering for data generated by single-mode and multi-mode distributions.
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| format | Article |
| id | doaj-art-0d7b6468530d4f4bbafea6f077746290 |
| institution | Kabale University |
| issn | 0132-2818 2335-898X |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Vilnius University Press |
| record_format | Article |
| series | Lietuvos Matematikos Rinkinys |
| spelling | doaj-art-0d7b6468530d4f4bbafea6f0777462902025-02-11T18:12:37ZengVilnius University PressLietuvos Matematikos Rinkinys0132-28182335-898X2023-09-0146spec.10.15388/LMR.2006.30726Research of nonparametric density estimation algorithms by applying clustering methodsRasa Šmidtaitė0Tomas Ruzgas1Kaunas University of TechnologyInstitute of Mathematics and Informatics One of the ways to improve the accuracy of probability density estimation is multi-mode density treating as the mixture of single-mode one. In this paper we offer to use data clustering in the first place and to estimate density in every cluster separately. To objectively compare the performance, Monte Carlo approximation is used. While using various methods to evaluate the accuracy of probability density estimations we tried to use clustered and not clustered data. In this paper we also tried to reveal the usefulness of using clustering for data generated by single-mode and multi-mode distributions. https://www.zurnalai.vu.lt/LMR/article/view/30726nonparametric density estimationsample clusteringMonte-Carlo method |
| spellingShingle | Rasa Šmidtaitė Tomas Ruzgas Research of nonparametric density estimation algorithms by applying clustering methods Lietuvos Matematikos Rinkinys nonparametric density estimation sample clustering Monte-Carlo method |
| title | Research of nonparametric density estimation algorithms by applying clustering methods |
| title_full | Research of nonparametric density estimation algorithms by applying clustering methods |
| title_fullStr | Research of nonparametric density estimation algorithms by applying clustering methods |
| title_full_unstemmed | Research of nonparametric density estimation algorithms by applying clustering methods |
| title_short | Research of nonparametric density estimation algorithms by applying clustering methods |
| title_sort | research of nonparametric density estimation algorithms by applying clustering methods |
| topic | nonparametric density estimation sample clustering Monte-Carlo method |
| url | https://www.zurnalai.vu.lt/LMR/article/view/30726 |
| work_keys_str_mv | AT rasasmidtaite researchofnonparametricdensityestimationalgorithmsbyapplyingclusteringmethods AT tomasruzgas researchofnonparametricdensityestimationalgorithmsbyapplyingclusteringmethods |