{"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/normalized-information-distance","title":"Normalized Information Distance","arxiv_id":"0809.2553","date":"2008-09-15","proceeding":null,"authors":["Paul M. B. Vitanyi","Frank J. Balbach","Rudi L. Cilibrasi","Ming Li"],"abstract":"The normalized information distance is a universal distance measure for\nobjects of all kinds. It is based on Kolmogorov complexity and thus\nuncomputable, but there are ways to utilize it. First, compression algorithms\ncan be used to approximate the Kolmogorov complexity if the objects have a\nstring representation. Second, for names and abstract concepts, page count\nstatistics from the World Wide Web can be used. These practical realizations of\nthe normalized information distance can then be applied to machine learning\ntasks, expecially clustering, to perform feature-free and parameter-free data\nmining. This chapter discusses the theoretical foundations of the normalized\ninformation distance and both practical realizations. It presents numerous\nexamples of successful real-world applications based on these distance\nmeasures, ranging from bioinformatics to music clustering to machine\ntranslation.","url_abs":"http://arxiv.org/abs/0809.2553v1","url_pdf":"http://arxiv.org/pdf/0809.2553v1.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":"normalized-information-distance","repo_url":"https://github.com/W95Psp/NID-results","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=0809.2553","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}