- Title
- Methods and applications of clusterwise linear regression : a survey and comparison
- Creator
- Long, Qiang; Bagirov, Adil; Taheri, Sona; Sultanova, Nargiz; Wu, Xue
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/193611
- Identifier
- vital:18210
- Identifier
-
https://doi.org/10.1145/3550074
- Identifier
- ISSN:1556-4681 (ISSN)
- Abstract
- Clusterwise linear regression (CLR) is a well-known technique for approximating a data using more than one linear function. It is based on the combination of clustering and multiple linear regression methods. This article provides a comprehensive survey and comparative assessments of CLR including model formulations, description of algorithms, and their performance on small to large-scale synthetic and real-world datasets. Some applications of the CLR algorithms and possible future research directions are also discussed. © 2023 Association for Computing Machinery.
- Publisher
- Association for Computing Machinery
- Relation
- ACM Transactions on Knowledge Discovery from Data Vol. 17, no. 3 (2023), p.; https://purl.org/au-research/grants/arc/DP190100580
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 Association for Computing Machinery
- Subject
- 4605 Data management and data science; 4606 Distributed computing and systems software; 4604 Cybersecurity and privacy; Cluster analysis; Clusterwise linear regression; Prediction methods; Regression analysis
- Reviewed
- Funder
- This work is supported by the National Natural Science Foundation of China (Grants No. 11501474, 61473320) and by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (Project No. DP190100580).
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