arXiv:2606.08322v1 Announce Type: new Abstract: To characterize the US airline profit cycles from 1995 to 2020, the authors of Renold et al. (2023) combine k-means clustering, principal component analysis, and system dynamic modelling. We replicate their clustering experiment in three spaces -- the original 7-dimensional raw-variable space, a 3-dimensional PC score space, and a 4-dimensional PC score space using their dataset gratefully included in the paper. We show that the six-cluster taxonomy is geometrically robust: k-means in 3-PC space produces bit-for-bit identical cluster assignments
This paper is being published as a new research output in the field of machine learning and statistical methods, indicating ongoing academic development.
For a sophisticated reader, this showcases incremental academic progress in applying established machine learning techniques to specific economic data sets, relevant for methodological refinement.
This research refines the understanding of clustering robustness in airline profit cycle analysis using PCA and Kernel PCA, offering methodological validation rather than substantive economic change.
- · Academic researchers in machine learning
- · Statisticians
This paper contributes to the academic discourse on the application and robustness of dimensionality reduction techniques in cluster analysis.
Improved methodological rigor in academic studies using similar techniques for economic or business analysis.
Potentially, better-validated analytical techniques could be adopted by industry for market segmentation or trend analysis, though the direct impact is minor.
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