{"type":"ScholarlyArticle","title":"Data Clustering: Principal Components, Hopfield and Self-Aggregation Networks","authors":[{"name":"Chris H. Q. Ding","slug":"chris-h-q-ding","given":"Chris H. Q.","family":"Ding","affiliation":""}],"abstract":"We present a coherent framework for data clustering~ Starting with a Hopfield network, we show the solutions for several well-motivated clustering objective functions are principal components. For MinMaxCut objectives motivated for ensuring cluster balance, the solutions are the nonlinearly scaled principal components. Using scaled PCA, we generalize to multi-way clustering, constructing a self-aggregation network, where connection weights between different clusters are automatically suppressed while connection weights within same clusters are automatically enhanced.","doi":null,"url":"https://ijcai.org/proceedings/2003/71","pdf":"https://ijcai.org/proceedings/2003/0071.pdf","markdown":"https://ijcai.org/proceedings/2003/71.md","bibtex":"https://ijcai.org/proceedings/2003/bibtex/71","volume":{"slug":"2003","title":"Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2003","url":"https://ijcai.org/proceedings/2003/","year":2003,"location":"Acapulco, Mexico","dates":"","editors":"Georg Gottlob","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":71,"pages":{"first":479,"last":483},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}