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Assisting Users with Clustering Tasks by Combining Metric Learning and Classification

Sumit Basu, Danyel Fisher, Steven M. Drucker, and Hao Lu

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.

Details

Publication typeInproceedings
Published inProceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI 2010)
PublisherAmerican Association for Artificial Intelligence
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