{"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/a-hash-based-co-clustering-algorithm-for","title":"A Hash-based Co-Clustering Algorithm for Categorical Data","arxiv_id":"1407.7753","date":"2014-07-29","proceeding":null,"authors":["Fabricio Olivetti de França"],"abstract":"Many real-life data are described by categorical attributes without a\npre-classification. A common data mining method used to extract information\nfrom this type of data is clustering. This method group together the samples\nfrom the data that are more similar than all other samples. But, categorical\ndata pose a challenge when extracting information because: the calculation of\ntwo objects similarity is usually done by measuring the number of common\nfeatures, but ignore a possible importance weighting; if the data may be\ndivided differently according to different subsets of the features, the\nalgorithm may find clusters with different meanings from each other,\ndifficulting the post analysis. Data Co-Clustering of categorical data is the\ntechnique that tries to find subsets of samples that share a subset of features\nin common. By doing so, not only a sample may belong to more than one cluster\nbut, the feature selection of each cluster describe its own characteristics. In\nthis paper a novel Co-Clustering technique for categorical data is proposed by\nusing Locality Sensitive Hashing technique in order to preprocess a list of\nCo-Clusters seeds based on a previous research. Results indicate this technique\nis capable of finding high quality Co-Clusters in many different categorical\ndata sets and scales linearly with the data set size.","url_abs":"http://arxiv.org/abs/1407.7753v1","url_pdf":"http://arxiv.org/pdf/1407.7753v1.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":"a-hash-based-co-clustering-algorithm-for","repo_url":"https://github.com/folivetti/HBLCoClust","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}