Assisting Users with Clustering Tasks by Combining Metric Learning and Classification
| Autor: | Sumit Basu, Danyel Fisher, Steven Drucker, Hao Lu |
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| Rok vydání: | 2010 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 24:394-400 |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v24i1.7694 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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