{"type":"ScholarlyArticle","title":"Affinity Learning for Mixed Data Clustering","authors":[{"name":"Nan Li","slug":"nan-li","given":"Nan","family":"Li","affiliation":"Department of Computer and Information Sciences Temple University , Philadelphia , USA"},{"name":"Longin Jan Latecki","slug":"longin-jan-latecki","given":"Longin Jan","family":"Latecki","affiliation":"Department of Computer and Information Sciences Temple University , Philadelphia , USA"}],"abstract":"In this paper, we propose a novel affinity learning based framework for mixed data clustering, which includes: how to process data with mixed-type attributes, how to learn affinities between data points, and how to exploit the learned affinities for clustering. In the proposed framework, each original data attribute is represented with several abstract objects defined according to the specific data type and values. Each attribute value is transformed into the initial affinities between the data point and the abstract objects of attribute. We refine these affinities and infer the unknown affinities between data points by taking into account the interconnections among the attribute values of all data points. The inferred affinities between data points can be exploited for clustering. Alternatively, the refined affinities between data points and the abstract objects of attributes can be transformed into new data features for clustering. Experimental results on many real world data sets demonstrate that the proposed framework is effective for mixed data clustering.","doi":"10.24963/ijcai.2017/302","url":"https://ijcai.org/proceedings/2017/302","pdf":"https://ijcai.org/proceedings/2017/0302.pdf","markdown":"https://ijcai.org/proceedings/2017/302.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/302","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":302,"pages":{"first":2173,"last":2179},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Ahmad and Dey, 2007] Amir Ahmad and Lipika Dey. 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