Comparison of mini-models based on various clustering algorithms

• Published in: Procedia computer science Vol. 176; pp. 3563 - 3570
• Main Author: Pietrzykowski, Marcin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2020
• Subjects: 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
• DOI: 10.1016/j.procs.2020.09.030

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.