Search Machine Learning Repository: @inproceedings{icml2014c2_carlsson14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Hierarchical Quasi-Clustering Methods for Asymmetric Networks},
    Url = {http://jmlr.org/proceedings/papers/v32/carlsson14.pdf},
    Abstract = {This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry of the input data. We show that this output structure is equivalent to a finite quasi-ultrametric space and study admissibility with respect to two desirable properties. We prove that a modified version of single linkage is the only admissible quasi-clustering method. Moreover, we show stability of the proposed method and we establish invariance properties fulfilled by it. Algorithms are further developed and the value of quasi-clustering analysis is illustrated with a study of internal migration within United States.},
    Author = {Gunnar Carlsson and Facundo Mémoli and Alejandro Ribeiro and Santiago Segarra},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {352-360}
   }