{"type":"ScholarlyArticle","title":"Central Clustering of Categorical Data with Automated Feature Weighting","authors":[{"name":"Lifei Chen","slug":"lifei-chen","given":"Lifei","family":"Chen","affiliation":""},{"name":"Shengrui Wang","slug":"shengrui-wang","given":"Shengrui","family":"Wang","affiliation":""}],"abstract":"The ability to cluster high-dimensional categorical data is essential for many machine learning applications such as bioinfomatics. Currently, central clustering of categorical data is a difficult problem due to the lack of a geometrically interpretable definition of a cluster center. In this paper, we propose a novel kernel-density-based definition using a Bayes-type probability estimator. Then, a new algorithm called k-centers is proposed for central clustering of categorical data, incorporating a new feature weighting scheme by which each attribute is automatically assigned with a weight measuring its individual contribution for the clusters. Experimental results on real-world data show outstanding performance of the proposed algorithm, especially in recognizing the biological patterns in DNA sequences.","doi":null,"url":"https://ijcai.org/proceedings/2013/190","pdf":"https://ijcai.org/proceedings/2013/0190.pdf","markdown":"https://ijcai.org/proceedings/2013/190.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/190","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":190,"pages":{"first":1260,"last":1265},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}