과제정보
This research was supported by Kyungpook National University Research Fund, 2021.
참고문헌
- Aitchison J (1982). The statistical analysis of compositional data, Journal of the Royal Statistical Society: Series B (Methodological), 44, 139-160. https://doi.org/10.1111/j.2517-6161.1982.tb01195.x
- Aitchison J (1986). The Statistical Analysis of Compositional Data (Monographs on Statistics and Applied Probability), Chapman and Hall London, New York.
- Aitchison J, Barcelo-Vidal C, Martin-Fernandez JA, and Pawlowsky-Glahn V (2000). Logratio analysis and compositional distance, Mathematical Geology, 32, 271-275. https://doi.org/10.1023/A:1007529726302
- Aitchison J (2008). The single principle of compositional data analysis, continuing fallacies, confusions and misunderstandings and some suggested remedies, In Proceedings of CoDaWork'08, The 3rd Compositional Data Analysis Workshop, Girona, Spain, Available from: http://hdl.handle.net/10256/706
- Buccianti A and Pawlowsky-Glahn V (2005). New perspectives on water chemistry and compositional data analysis, Mathematical Geology, 37, 703-727. https://doi.org/10.1007/s11004-005-7376-6
- Cook (1964). Percentage Baseball, Waverly Press, Brooklyn, New York City.
- Cust EE, Sweeting AJ, Ball K, and Robertson S (2019). Machine and deep learning for sport-specific movement recognition: A systematic review of model development and performance, Journal of Sports Sciences, 37, 568-600. https://doi.org/10.1080/02640414.2018.1521769
- Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, and Barcelo-Vidal C (2003). Isometric logratio transformations for compositional data analysis, Mathematical Geology, 35, 279-300. https://doi.org/10.1023/A:1023818214614
- Ester M, Kriegel H-P, Sander J, and Xu X (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, In KDD, 96, 226-231.
- Galletti A and Maratea A (2016). Numerical stability analysis of the centered log-ratio transformation, In Proceedings of 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Napoli, 713-716. IEEE.
- Godichon-Baggioni A, Maugis-Rabusseau C, and Rau A (2019). Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data, Journal of Applied Statistics, 46, 47-65. https://doi.org/10.1080/02664763.2018.1454894
- Greenacre M, Martinez-Alvaro M, and Blasco A (2021). Compositional data analysis of microbiome and any-omics datasets: A validation of the additive logratio transformation, Frontiers in Microbiology, 12, 727398.
- Hron K, Templ M, and Filzmoser P (2010). Imputation of missing values for compositional data using classical and robust methods, Computational Statistics & Data Analysis, 54, 3095-3107. https://doi.org/10.1016/j.csda.2009.11.023
- Kucera M and Malmgren BA (1998). Logratio transformation of compositional data: A resolution of the constant sum constraint, Marine Micropaleontology, 34, 117-120. https://doi.org/10.1016/S0377-8398(97)00047-9
- Li H (2015). Microbiome, metagenomics, and high-dimensional compositional data analysis, Annual Review of Statistics and Its Application, 2, 73-94.
- Mardia KV and Jupp PE (2000). Directional Statistics, Wiley Online Library.
- Ordonez EG, Perez MdCI, and Gonzalez CT (2016). Performance assessment in water polo using compositional data analysis, Journal of Human Kinetics, 54, 143-151. https://doi.org/10.1515/hukin-2016-0043
- Palarea-Albaladejo J, Martin-Fernandez JA, and Soto JA (2012). Dealing with distances and transformations for fuzzy C-means clustering of compositional data, Journal of Classification, 29, 144-169. https://doi.org/10.1007/s00357-012-9105-4
- Rein R and Memmert D (2016). Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science, SpringerPlus, 5, 1-13. https://doi.org/10.1186/s40064-015-1659-2
- Scealy J and Welsh A (2011). Regression for compositional data by using distributions defined on the hypersphere, Journal of the Royal Statistical Society Series B: Statistical Methodology, 73, 351-375. https://doi.org/10.1111/j.1467-9868.2010.00766.x
- Scealy J and Welsh AH (2014). Fitting Kent models to compositional data with small concentration, Statistics and Computing, 24, 165-179. https://doi.org/10.1007/s11222-012-9361-5
- Schubert E, Sander J, Ester M, Kriegel HP, and Xu X (2017). Dbscan revisited, revisited: Why and how you should (still) use dbscan, ACM Transactions on Database Systems (TODS), 42, 1-21.
- Shen J, Hao X, Liang Z, Liu Y, Wang W, and Shao L (2016). Real-time superpixel segmentation by dbscan clustering algorithm, IEEE Transactions on Image Processing, 25, 5933-5942. https://doi.org/10.1109/TIP.2016.2616302
- Wang Z, Shi W, Zhou W, Li X, and Yue T (2020). Comparison of additive and isometric log-ratio transformations combined with machine learning and regression kriging models for mapping soil particle size fractions, Geoderma, 365, 114214.