{"type":"ScholarlyArticle","title":"Descriptive Clustering: ILP and CP Formulations with Applications","authors":[{"name":"Thi-Bich-Hanh Dao","slug":"thi-bich-hanh-dao","given":"Thi-Bich-Hanh","family":"Dao","affiliation":"LIFO , University of Orléans , France"},{"name":"Chia-Tung Kuo","slug":"chia-tung-kuo","given":"Chia-Tung","family":"Kuo","affiliation":"University of California , Davis"},{"name":"S. S. Ravi","slug":"s-s-ravi","given":"S. S.","family":"Ravi","affiliation":"Virginia Tech; University at Albany -SUNY"},{"name":"Christel Vrain","slug":"christel-vrain","given":"Christel","family":"Vrain","affiliation":"LIFO , University of Orléans , France"},{"name":"Ian Davidson","slug":"ian-davidson","given":"Ian","family":"Davidson","affiliation":"University of California , Davis"}],"abstract":"In many settings just finding a good clustering is insufficient and\r\nan explanation of the clustering is required. If the features used\r\nto perform the clustering are interpretable then methods such as\r\nconceptual clustering can be used. However, in many applications\r\nthis is not the case particularly for image, graph and other complex\r\ndata. Here we explore the setting where a set of interpretable\r\ndiscrete tags for each instance is available. We  formulate the\r\ndescriptive clustering problem as a bi-objective optimization\r\nto simultaneously find compact clusters using the features and to\r\ndescribe them using the tags. We present our formulation in a\r\ndeclarative platform and show it can be integrated into a standard\r\niterative algorithm to find all Pareto optimal solutions to the two\r\nobjectives.  Preliminary results  demonstrate the utility of our\r\napproach on real data sets for images and electronic health care\r\nrecords and that it outperforms single objective and multi-view clustering\r\nbaselines.","doi":"10.24963/ijcai.2018/176","url":"https://ijcai.org/proceedings/2018/176","pdf":"https://ijcai.org/proceedings/2018/0176.pdf","markdown":"https://ijcai.org/proceedings/2018/176.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/176","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Constraints and SAT","track":"Main track","number":176,"pages":{"first":1263,"last":1269},"keywords":["Constraints and SAT: Modeling;Formulation","Machine Learning: Clustering","Constraints and SAT: Constraints and Data Mining ; Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Babaki et al., 2014] Behrouz Babaki, Tias Guns, and Siegfried Nijssen. 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