{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/simultaneous-coherent-structure-coloring","title":"Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity","arxiv_id":"1807.04427","date":"2018-07-12","proceeding":null,"authors":["Brooke E. Husic","Kristy L. Schlueter-Kuck","John O. Dabiri"],"abstract":"The clustering of data into physically meaningful subsets often requires\nassumptions regarding the number, size, or shape of the subgroups. Here, we\npresent a new method, simultaneous coherent structure coloring (sCSC), which\naccomplishes the task of unsupervised clustering without a priori guidance\nregarding the underlying structure of the data. sCSC performs a sequence of\nbinary splittings on the dataset such that the most dissimilar data points are\nrequired to be in separate clusters. To achieve this, we obtain a set of\northogonal coordinates along which dissimilarity in the dataset is maximized\nfrom a generalized eigenvalue problem based on the pairwise dissimilarity\nbetween the data points to be clustered. This sequence of bifurcations produces\na binary tree representation of the system, from which the number of clusters\nin the data and their interrelationships naturally emerge. To illustrate the\neffectiveness of the method in the absence of a priori assumptions, we apply it\nto three exemplary problems in fluid dynamics. Then, we illustrate its capacity\nfor interpretability using a high-dimensional protein folding simulation\ndataset. While we restrict our examples to dynamical physical systems in this\nwork, we anticipate straightforward translation to other fields where existing\nanalysis tools require ad hoc assumptions on the data structure, lack the\ninterpretability of the present method, or in which the underlying processes\nare less accessible, such as genomics and neuroscience.","url_abs":"http://arxiv.org/abs/1807.04427v3","url_pdf":"http://arxiv.org/pdf/1807.04427v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"simultaneous-coherent-structure-coloring","repo_url":"https://github.com/brookehus/sCSC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"protein-folding","task_name":"Protein Folding"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}