@inproceedings{f9c49f730f5142a1a9f2f3322b7894eb,
title = "A supervisory approach to semi-supervised clustering",
abstract = "We propose a new approach to semi-supervised clustering that utilizes boosting to simultaneously learn both a similarity measure and a clustering of the data from given instancelevel must-link and cannot-link constraints. The approach is distinctive in that it uses a supervising feedback loop to gradually update the similarity while at the same time guiding an underlying unsupervised clustering algorithm. Our approach is grounded in the theory of boosting. We provide three examples of the clustering algorithm on real datasets.",
keywords = "Algorithms, Clustering methods, Learning systems, Pattern classification",
author = "Bryan Conroy and Xi, {Yongxin Taylor} and Peter Ramadge",
note = "Copyright: Copyright 2010 Elsevier B.V., All rights reserved.; 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 ; Conference date: 14-03-2010 Through 19-03-2010",
year = "2010",
doi = "10.1109/ICASSP.2010.5495368",
language = "English (US)",
isbn = "9781424442966",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1858--1861",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",
address = "United States",
}