{"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/interactive-visual-data-exploration-with","title":"Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach","arxiv_id":"1710.08167","date":"2017-10-23","proceeding":null,"authors":["Kai Puolamäki","Emilia Oikarinen","Bo Kang","Jefrey Lijffijt","Tijl De Bie"],"abstract":"Visual exploration of high-dimensional real-valued datasets is a fundamental\ntask in exploratory data analysis (EDA). Existing methods use predefined\ncriteria to choose the representation of data. There is a lack of methods that\n(i) elicit from the user what she has learned from the data and (ii) show\npatterns that she does not know yet. We construct a theoretical model where\nidentified patterns can be input as knowledge to the system. The knowledge\nsyntax here is intuitive, such as \"this set of points forms a cluster\", and\nrequires no knowledge of maths. This background knowledge is used to find a\nMaximum Entropy distribution of the data, after which the system provides the\nuser data projections in which the data and the Maximum Entropy distribution\ndiffer the most, hence showing the user aspects of the data that are maximally\ninformative given the user's current knowledge. We provide an open source EDA\nsystem with tailored interactive visualizations to demonstrate these concepts.\nWe study the performance of the system and present use cases on both synthetic\nand real data. We find that the model and the prototype system allow the user\nto learn information efficiently from various data sources and the system works\nsufficiently fast in practice. We conclude that the information theoretic\napproach to exploratory data analysis where patterns observed by a user are\nformalized as constraints provides a principled, intuitive, and efficient basis\nfor constructing an EDA system.","url_abs":"http://arxiv.org/abs/1710.08167v1","url_pdf":"http://arxiv.org/pdf/1710.08167v1.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":"interactive-visual-data-exploration-with","repo_url":"https://github.com/edahelsinki/EDAdemoR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}