AAAI Publications, Twenty-Second International FLAIRS Conference

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Hidden Markov Random Fields Based LSI Text Semi-supervised Clustering
Kerui Min, Gang Liu, Xin Chen, Shengqi Lu

Last modified: 2009-03-17

Abstract


Semi-supervised learning is an active research field. Previous results shown that unite background information into the original unsupervised clustering problem could archive higher accuracy. In this paper, we explore the cooperation between the pairwise constrains given by the user and the sematic information in natural language. In addition, we reduce the time complexity to make the algorithm feasible for large quantities of data. Experiments on different scales of corpus show the robustness and effectiveness of the proposed algorithm, which the F-measure archives 20% higher than previous algorithms.

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