Comparison of mini-models based on various clustering algorithms
The article deals with the subject of mini-models (MMs) based on clustering algorithms. The mini-model method is a local regression algorithm that operates on some part of the input space called the mini-model domain (MM domain). MM domain can be created as a multidimensional polytope in the input s...
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| Veröffentlicht in: | Procedia computer science Jg. 176; S. 3563 - 3570 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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2020
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| Abstract | The article deals with the subject of mini-models (MMs) based on clustering algorithms. The mini-model method is a local regression algorithm that operates on some part of the input space called the mini-model domain (MM domain). MM domain can be created as a multidimensional polytope in the input space. Another possible solution is to divide the input space with clustering algorithms. As a result of this process, each data cluster is treated as a separate mini-model domain. The main aim of the article is to create an exhaustive comparison of mini-model methods based on the most well-known clustering algorithms. The work introduces new versions of the mini-model method based on clustering algorithms such as DBSCAN, OPTICS, Mean Shift, spectral clustering and several hierarchical methods. The paper also compares the results with other versions of the MM-method and instance-based learning algorithms. |
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| AbstractList | The article deals with the subject of mini-models (MMs) based on clustering algorithms. The mini-model method is a local regression algorithm that operates on some part of the input space called the mini-model domain (MM domain). MM domain can be created as a multidimensional polytope in the input space. Another possible solution is to divide the input space with clustering algorithms. As a result of this process, each data cluster is treated as a separate mini-model domain. The main aim of the article is to create an exhaustive comparison of mini-model methods based on the most well-known clustering algorithms. The work introduces new versions of the mini-model method based on clustering algorithms such as DBSCAN, OPTICS, Mean Shift, spectral clustering and several hierarchical methods. The paper also compares the results with other versions of the MM-method and instance-based learning algorithms. |
| Author | Pietrzykowski, Marcin |
| Author_xml | – sequence: 1 givenname: Marcin surname: Pietrzykowski fullname: Pietrzykowski, Marcin email: mpietrzykowski@wi.zut.edu.pl organization: Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Żołnierska 49, 71-210 Szczecin, Poland |
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| Cites_doi | 10.1109/TNNLS.2017.2777489 10.3390/sym12040516 10.1214/12-AOS1049 10.3390/su11123314 10.1145/3068335 10.1016/j.patcog.2016.07.007 10.1016/j.neucom.2016.01.102 10.1007/978-3-319-39384-1_32 10.1016/j.jprocont.2016.07.009 10.1016/j.procs.2019.09.426 10.1109/TNNLS.2017.2673241 10.1109/TIP.2014.2365720 10.1016/j.patrec.2009.09.011 10.1109/TMECH.2014.2358674 10.1016/j.eswa.2008.01.039 10.1109/TIP.2016.2616302 10.1145/304181.304187 10.1016/j.procs.2017.08.210 10.1016/j.enconman.2016.05.061 10.1007/978-3-319-19369-4_41 10.3390/en13092155 10.1016/j.neucom.2016.01.093 10.1049/iet-cvi.2016.0022 10.1016/j.neucom.2012.10.043 10.1016/j.ins.2017.07.010 10.1109/TNNLS.2016.2608001 10.1016/j.knosys.2014.11.013 10.1016/j.measurement.2016.02.037 10.1109/TKDE.2017.2650229 |
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| Keywords | clustering methods local self-learning function approximation instance-based learning mini-model |
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