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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Vydáno v:Procedia computer science Ročník 176; s. 3563 - 3570
Hlavní autor: Pietrzykowski, Marcin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 2020
Témata:
clustering methods
function approximation
instance-based learning
local self-learning
mini-model
clustering methods
local self-learning
function approximation
instance-based learning
mini-model
ISSN:1877-0509, 1877-0509
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Shrnutí: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2020.09.030