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

被引：0

作者：

Zhao, Weizhong ^{[1
]}

He, Qing ^{[1
]}

Ma, Huifang ^{[1
]}

Shi, Zhongzhi ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China

来源：

2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 | 2009年

关键词：

Semi-supervised Clustering; Document Clustering; Active Learning; Instance-level Constraint;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：264 / 268

页数：5

共 13 条

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