Active Learning of Instance-Level Constraints for Semi-supervised Document Clustering

Authors:
Weizhong Zhao;Qing He;Huifang Ma;Zhongzhi Shi
Affiliations:
-;-;-;-
Venue:
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Year:
2009

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Abstract

This paper presents a framework that actively selects informative documents pairs for semi-supervised document clustering. The semi-supervised document clustering algorithm is a Constrained DBSCAN (Cons-DBSCAN), which incorporates instance-level constraints to guide the clustering process in DBSCAN. By obtaining user feedbacks, our proposed active learning algorithm can get informative instance level constraints to aid clustering process. Experimental results show that Cons-DBSCAN with the proposed active learning approach can provide an appealing clustering performance.