@inproceedings{basu2010assisting,
author = {Basu, Sumit and Fisher, Danyel and Drucker, Steven and Lu, Hao},
title = {Assisting Users with Clustering Tasks by Combining Metric Learning and Classification},
booktitle = {Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI 2010)},
year = {2010},
month = {July},
abstract = {Interactive clustering refers to situations in which a human labeler is willing to assist a learning algorithm in automatically clustering items. We present a related but somewhat different task, assisted clustering, in which a user creates explicit groups of items from a large set and wants suggestions on what items to add to each group. While the traditional approach to interactive clustering has been to use metric learning to induce a distance metric, our situation seems equally amenable to classification. Using clusterings of documents from human subjects, we found that one or the other method proved to be superior for a given cluster, but not uniformly so. We thus developed a hybrid mechanism for combining the metric learner and the classifier. We present results from a large number of  trials based on human clusterings, in which we show that our combination scheme matches and often exceeds the performance of a method which exclusively uses either type of learner.},
publisher = {American Association for Artificial Intelligence},
url = {http://approjects.co.za/?big=en-us/research/publication/assisting-users-with-clustering-tasks-by-combining-metric-learning-and-classification/},
edition = {Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI 2010)},
}