{"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/spectrometric-differentiation-of-yeast","title":"Spectrometric differentiation of yeast strains using minimum volume increase and minimum direction change clustering criteria","arxiv_id":null,"date":"2014-03-28","proceeding":null,"authors":["Nuno Fachada","Mário A. T. Figueiredo","Vitor V. Lopes","Rui C. Martins","Agostinho C.Rosa"],"abstract":"This paper proposes new clustering criteria for distinguishing Saccharomyces cerevisiae (yeast) strains using their spectrometric signature. These criteria are introduced in an agglomerative hierarchical clustering context, and consist of: (a) minimizing the total volume of clusters, as given by their respective convex hulls; and, (b) minimizing the global variance in cluster directionality. The method is deterministic and produces dendrograms, which are important features for microbiologists. A set of experiments, performed on yeast spectrometric data and on synthetic data, show the new approach outperforms several well-known clustering algorithms, including techniques commonly used for microorganism differentiation.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S0167865514000889","url_pdf":"https://www.researchgate.net/profile/Nuno_Fachada/publication/261172243_Spectrometric_differentiation_of_yeast_strains_using_minimum_volume_increase_and_minimum_direction_change_clustering_criteria/links/59e4faeba6fdcc1b1d8d235c/Spectrometric-differentiation-of-yeast-strains-using-minimum-volume-increase-and-minimum-direction-change-clustering-criteria.pdf?_sg%5B0%5D=fY8jWKgEaEFNkYutmgt0i5DSnxj9vXdRiRGf09GRULb6BaBB2KDQp2L8w6n9PRdRk2FtJ0TRGj7uM4Z7xe2XPQ.ONZAZLCIdJrbm9NEJ_ycEAwGiMuOa2Kocmdj31ThzPBf5PRBf7-xH1q00RB_YCyE62f1BQzMvx1M3a1PzdGTMw&_sg%5B1%5D=te6VUhf4eTtDK3tfJC0UJW4Ua00l1rmbi6gxfm3Q0T5A7tKsbLUI199E17Wq_KVzfi6DLPWW4gMsTG3C4qG3N8mI73Q-3f76ryc_jTU2eWQE.ONZAZLCIdJrbm9NEJ_ycEAwGiMuOa2Kocmdj31ThzPBf5PRBf7-xH1q00RB_YCyE62f1BQzMvx1M3a1PzdGTMw&_sg%5B2%5D=gIRBHaRxSqI9YYGJme96JLqSweB8o3HnJZagZnEfdk2B0TGq7KTPjBGOm4ozii4r3L20jf0uVOy-J6Y.sIoufaMpcXONRMqZ6ZVdXbR4kGZtLYpRLi2XuyTy6d5WLTYqk0ykhTYTODko-KRV-7vFiqvkAiBKIgSze2VHJQ&_iepl=","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":"spectrometric-differentiation-of-yeast","repo_url":"https://github.com/fakenmc/amvidc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}