| Autor: |
QIANG LONG, BAGIROV, ADIL, TAHERI, SONA, SULTANOVA, NARGIZ, XUE WU |
| Předmět: |
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| Zdroj: |
ACM Transactions on Knowledge Discovery from Data; Apr2023, Vol. 17 Issue 3, p1-54, 54p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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