References
Abascal, R. (1974). Análisis por Activación de Neutrones: Una Aportación para la Arqueología Moderna, B.A. Thesis in Archaeology, Escuela Nacional de Antropologia e Historia, Mexico City, Mexico.
Aitchison, J. (1986). The Statistical Analysis of Compositional Data. London: Chapman & Hall.
Argote-Espino, D. L., Solé, J., Sterpone, O. & López García, P. (2010). Análisis composicional de seis yacimientos de obsidiana del centro de México y su clasificación con DBSCAN. Arqueología, 43, 197–215.
Argote-Espino, D. L., Solé, J., López-García, P. & Sterpone, O. (2012). Obsidian sub-source identification in the Sierra de Pachuca and Otumba volcanic regions, Central Mexico, by ICP-MS and DBSCAN statistical analysis. Geoarchaeology, 27, 48–62.
Aubert, A. H., Thrun, M. C., Breuer, L. & Ultsch, A. (2016). Knowledge discovery from high-frequency stream nitrate concentrations: Hydrology and biology contributions. Scientific Reports, 6, 31536.
Biernacki, C., Marbac, M. & Vandewalle, V. (2021). Gaussian-based visualization of gaussian and non-gaussian-based clustering. Journal of Classification, 38(1), 129–157.
Brambila, R. (1988). Los estudios de la cerámica Anaranjada Delgada: ensayo bibliográfico. In Puche, M. C. Serra and Navarrete, C., coords., Ensayos de Alfarería prehispánica e histórica de Mesoamérica. Homenaje a Eduardo Noguera Auza. México: Instituto de Investigaciones Antropológicas and Universidad Nacional Autónoma de México, pp. 221–247.
Brinkmann, L., Stier, Q. & Thrun, M. C. (2023). Computing sensitive color transitions for the identification of two-dimensional structures. In Proceedings of Data Science, Statistics & Visualisation and the European Conference on Data Analysis. Antwerp: University of Antwerp, DSSV-ECDA, pp. 57.
Callaghan, M. G., Pierce, D. E., Kovacevich, B. & Glascock, M. D. (2017). Chemical paste characterization of late middle Preclassic-period ceramics from Holtun, Guatemala and its implications for production and exchange. Journal of Archaeological Science: Reports, 12, 334–345.
Carr, S. (2015). Geochemical Characterization of Obsidian Subsources in Highland Guatemala, B.A. Thesis, Pennsylvania State University, US.
Cavallo, M. & Demiralp, Ç. (2018). Clustrophile 2: Guided visual clustering analysis. IEEE Transactions on Visualization and Computer Graphics, 25(1), 267–276.
Choo, J., Lee, H., Liu, Z., Stasko, J. & Park, H. (2013). An interactive visual testbed system for dimension reduction and clustering of large-scale high-dimensional data. In Proceedings of SPIE-IS and T Electronic Imaging – Visualization and Data Analysis 2013 [8654]. Burlingame, CA: The International Society for Optics and Photonics, 865402.
Cobean, R. H. (2002). A World of Obsidian: The Mining and Trade of a Volcanic Glass in Ancient Mexico. Mexico: Instituto Nacional de Antropologia e Historia and Pittsburgh University.
Cook de Leonard, C. (1953). Los popolocas de Puebla (ensayo de una identificación etnodemográfica e histórico arqueológica), Huastecos, Totonacos y sus vecinos. Revista Mexicana de Estudios Antropológicos, 13(2–3), 423–445.
Dasgupta, S. & Gupta, A. (2003). An elementary proof of a theorem of Johnson and Lindenstrauss. Random Structures & Algorithms, 22(1), 60–65.
Davies, D. L. & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), 224–227.
Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95–104.
Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barceló-Vidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279–300.
Glascock, M. D., Braswell, G. E. & Cobean, R. H. (1998). A systematic approach to obsidian source characterization. In Shackley, M. S., ed., Archaeological Obsidian Studies. Vol 3 of Advances in Archaeological and Museum Science. Boston, MA: Springer, pp. 15–65.
Grün, B. (2019). Chapter 8: Model-based clustering. In Frühwirth-Schnatter, S., Celeux, G., and Robert, C. P., eds., Handbook of Mixture Analysis. Boca Raton, FL: Chapman and Hall/CRC Press, pp. 1–36.
Harbottle, G., Sayre, E. V. & Abascal, R. (1976). Neutron Activation Analysis of Thin Orange Pottery. Upton, NY: Brookhaven National Lab.
Holzinger, A. (2018). From machine learning to explainable AI. IEEE, Proceedings of the 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), 55–66.
Holzinger, A., Plass, M., Kickmeier-Rust, M. et al. (2019). Interactive machine learning: Experimental evidence for the human in the algorithmic loop. Applied Intelligence, 49(7), 2401–2414.
Hubert, M., Rousseeuw, P. J. & Vanden Branden, K. (2005). ROBPCA: A new approach to robust principal component analysis. Technometrics, 47(1), 64–79.
Hunt, A. M. W. & Speakman, R. J. (2015). Portable XRF analysis of archaeological sediments and ceramics. Journal of Archaeological Science, 53, 626–638.
Jain, A. K. & Dubes, R. C. (1988). Algorithms for Clustering Data. Vol. 3. Englewood Cliffs, NJ: Prentice Hall College Division.
Jeong, D. H., Ziemkiewicz, C., Fisher, B., Ribarsky, W. & Chang, R. (2009). iPCA: An Interactive system for PCA‐based visual analytics. Computer Graphics Forum, 28(3), 767–774.
Johnson, W. B. & Lindenstrauss, J. (1984). Extensions of Lipschitz mappings into a Hilbert space. Contemporary Mathematics, 26(1), 189–206.
Kaufman, L. & Rousseeuw, P. J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, NJ: Wiley-Interscience.
Kolb, C. (1973). Thin Orange Pottery at Teotihuacan. In Sanders, W., ed., Miscellaneous Papers in Anthropology, Vol. 8. Pennsylvania: Pennsylvania State University, pp. 309–377.
Kucheryavskiy, S. (2020). mdatools – R package for chemometrics. Chemometrics and Intelligent Laboratory Systems, 198, 103937.
Kwon, B. C., Eysenbach, B., Verma, J. et al. (2017). Clustervision: Visual supervision of unsupervised clustering. IEEE Transactions on Visualization and Computer Graphics, 24(1), 142–151.
Lebret, R., Lovleff, S., Langrognet, F. et al. (2015). Rmixmod: The R package of the model-based unsupervised, supervised, and semi-supervised classification Mixmod library. Journal of Statistical Software, 67(6), 1–29.
Linné, S. (2003). Mexican Highland Cultures: Archaeological Researches at Teotihuacan, Calpulalpan and Chalchicomula in 1934–1935. Alabama: The University of Alabama Press.
López-García, P. A. & Argote, D. L. (2023). Cluster analysis for the selection of potential discriminatory variables and the identification of subgroups in archaeometry. Journal of Archaeological Science: Reports, 49, 104022.
López-García, P., Argote, D. L. & Beirnaert, C. (2019). Chemometric analysis of Mesoamerican obsidian sources. Quaternary International, 510, 100–118.
López-García, P., Argote, D. L. & Thrun, M. C. (2020). Projection-based classification of chemical groups and provenance analysis of archaeological materials. IEEE Access, 8, 152439–152451.
López Luján, L., Neff, H. & Sugiyama, S. (2000). The 9-Xi Vase: A Classic Thin Orange vessel found at Tenochtitlan. In Carrasco, D., Jones, L., and Sessions, S., coords., Mesoamerica’s Classic Heritage: From Teotihuacan to the Aztecs. Boulder, CO: University Press of Colorado, pp. 219–249.
Lukas-Tooth, H. J. & Price, B. J. (1961). A mathematical method for the investigation of interelement effects in x-ray fluorescence analysis. Metallurgia, 64(2), 149–152.
Mac Aodha, O., Stathopoulos, V., Brostow, G. J., et al. (2014). Putting the scientist in the loop–accelerating scientific progress with interactive machine learning. In Proceedings of the 2014 22nd International Conference on Pattern Recognition. Stockholm: IEEE, pp. 9–17.
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. (2022). Cluster: Cluster Analysis Basics and Extensions. R Package Version 2.1.4. https://CRAN.R-project.org/package=cluster (Accessed: August 03, 2023). Minc, L. D., Sherman, R. J., Elson, C., et al. (2016). Ceramic provenance and the regional organization of pottery production during the later Formative periods in the Valley of Oaxaca, Mexico: Results of trace-element and mineralogical analyses. Journal of Archaeological Science: Reports, 8, 28–46.
Müller, F., 1978. La Cerámica del Centro Ceremonial de Teotihuacán. México: Instituto Nacional de Antropología e Historia.
Müller, E., Assent, I., Krieger, R., Jansen, T. & Seidl, T. (2008). Morpheus: Interactive exploration of subspace clustering. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, NV: ACM, pp. 1089–1092.
Murtagh, F. (2004). On ultrametricity, data coding, and computation. Journal of Classification, 21(2), 167–184.
Nance, R. D., Mille, B. V., Keppie, J. D., Murphy, J. B. & Dostal, J. (2006). Acatlán Complex, southern Mexico: Record spanning the assembly and breakup of Pangea. Geology, 34(10), 857–860.
Parent, S. É., Parent, L. E., Rozanne, D. E., Hernandes, A. & Natale, W. (2012). Chapter 4: Nutrient balance as paradigm of soil and plant chemometrics. In Issaka, R. Nuhu, ed., Soil Fertility. London: InTechOpen, pp. 83–114.
Partovi Nia, V. & Davison, A. C. (2012). High-dimensional bayesian clustering with variable selection: The R package bclust. Journal of Statistical Software, 47(5), 1–22.
Pawlowsky-Glahn, V., Egozcue, J. (2011). Exploring compositional data with the CoDa-dendrogram. Austrian Journal of Statistics, 40(1–2), 103–113.
R Development Core Team (2011). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. www.R-project.org (Accessed: November 24, 2016). Rasmussen, M. & Karypis, G. (2004). Gcluto: An interactive clustering, visualization, and analysis system. CSE/UMN Technical Report: TR# 04–021, Department of Computer Science & Engineering, University of Minnesota, Minnesota, USA. https://hdl.handle.net/11299/215615 (Accessed: November 24, 2016). Rattray, E. C. (1979). La cerámica de Teotihuacan: relaciones externas y cronología. Anales de Antropología, 16, 51–70.
Rattray, E. C. (2001). Teotihuacan: Ceramics, Chronology and Cultural Trends. Mexico: Instituto Nacional de Antropología e Historia – University of Pittsburgh.
Rattray, E. C. & Harbottle, G. (1992). Neutron Activation Analysis and numerical taxonomy of Thin Orange ceramics from the manufacturing sites of Rio Carnero, Puebla, Mexico. In Neff, H., ed., Chemical Characterization of Ceramic Pastes in Archaeology. Madison, WI: Prehistory Press, pp. 221–231.
Rousseeuw, P. J. & van Zomeren, B. C. (1990). Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association, 85(411), 633–639.
Rowe, H., Hughes, N. & Robinson, K. (2012). The quantification and application of handheld energy-dispersive X-ray fluorescence (ED-XRF) in Mudrock Chemostratigraphy and Geochemistry. Chemical Geology, 324–325, 122–131.
Ruvalcaba-Sil, J. L., Ontalba Salamanca, M. A., Manzanilla, L. et al. (1999). Characterization of pre-Hispanic pottery from Teotihuacan, Mexico, by a combined PIXE-RBS and XRD analysis. Nuclear Instruments and Methods in Physics Research B: Beam Interactions with Materials and Atoms, 150(1–4), 591–596.
Shepard, A. O. (1946). Technological features of Thin Orange Ware. In Kidder, A., Jenning, J., and Shook, E., eds., Excavations at Kaminaljuyu. Publication 561. Washington, DC: Carnegie Institution of Washington, pp. 198–201.
Sotomayor, A. & Castillo, N. (1963). Estudio Petrográfico de la Cerámica “Anaranjada Delgada”. Publicaciones del Departamento de Prehistoria no. 12. México: Instituto Nacional de Antropología e Historia.
Stoner, W. D. (2016). The analytical nexus of ceramic paste composition studies: A comparison of NAA, LA-ICP-MS, and petrography in the prehispanic Basin of Mexico. Journal of Archaeological Science, 76, 31–47.
Thrun, M. C. (2018). Projection Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data (Ultsch, A. & Hüllermeier, E., eds.). Heidelberg: Springer Vieweg.
Thrun, M. C. (2021a). The exploitation of distance distributions for clustering. International Journal of Computational Intelligence and Applications, 20(3), 2150016.
Thrun, M. C. (2021b). Distance-based clustering challenges for unbiased benchmarking studies. Scientific Reports, 11, 18988.
Thrun, M. C. (2022). Exploiting distance-based structures in data using an explainable AI for stock picking. Information, 13(2), 51
Thrun, M. C. & Ultsch, A. (2021). Swarm intelligence for self-organized clustering. Artificial Intelligence, 290, 103237.
Thrun, M. C., Lerch, F., Lötsch, J. & Ultsch, A. (2016). Visualization and 3D printing of multivariate data of biomarkers. In Skala, V., ed., 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG). Plzen, Czech Republic, pp. 7–16.
Thrun, M. C., Pape, F. & Ultsch, A. (2020). Interactive machine learning tool for clustering in visual analytics. In 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA). Sydney: IEEE, pp. 672–680.
Thrun, M. C., Pape, F. & Ultsch, A. (2021a). Conventional displays of structures in data compared with interactive projection-based clustering (IPBC). International Journal of Data Science and Analytics, 12(3), 249–271.
Thrun, M. C., Ultsch, A., & Breuer, L. (2021b). Explainable AI framework for multivariate hydrochemical time series. Machine Learning and Knowledge Extraction, 3(1), 170–205.
Thrun, M. C., Mack, E., Neubauer, A. et al. (2022). A bioinformatics view on acute myeloid leukemia surface molecules by combined Bayesian and ABC analysis. Bioengineering, 9(11), 642.
Thrun, M. C., Märte, J. & Stier, Q. (2023). Analyzing quality measurements for dimensionality reduction. Machine Learning and Knowledge Extraction, 5(3), 1076–1118.
Todorov, V. & Filzmoser, P. (2009). An object-oriented framework for robust multivariate analysis. Journal of Statistical Software, 32(3), 1–47.
Ultsch, A. (1999). Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series. In Oja, E. and Kaski, S., eds., Kohonen Maps. Amsterdam: Elsevier Science B.V., pp. 33–46.
Ultsch, A. (2003). U*-matrix: A tool to Visualize Clusters in High Dimensional Data. Technical report 36, Department of Mathematics and Computer Science, University of Marburg, Germany.
Ultsch, A. & Siemon, H. P. (1990). Kohonen’s self-organizing feature maps for exploratory data analysis. In Proceedings of the International Neural Network Conference. Paris: Kluwer Academic Press, pp. 305–308.
van den Boogaart, K. G. & Tolosana-Delgado, R. (2013). Analyzing Compositional Data with R. Heidelberg: Springer-Verlag.
Wilkinson, L. & Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63(2), 179–184.
Wiwie, C., Baumbach, J. & Röttger, R. (2015). Comparing the performance of biomedical clustering methods. Nature Methods, 12, 1033–1038.
Yang, Y., Kandogan, E., Li, Y., Sen, P. & Lasecki, W. S. (2019). A study on interaction in human-in-the-loop machine learning for text analytics. In Proceedings of the ACM IUI Workshops ‘19. Los Angeles, CA: ACM, pp. 1–7.
Zanzotto, F. M. (2019). Human-in-the-loop artificial intelligence. Journal of Artificial Intelligence Research, 64, 243–252.
Zhang, L., Tang, C., Shi, Y. et al. (2002). VizCluster: An interactive visualization approach to cluster analysis and its application on microarray data. In Proceedings of the 2002 SIAM International Conference on Data Mining (SDM). Philadelphia, PA: Society for Industrial and Applied Mathematics, pp. 19–40.
