Abstract
Hierarchical co-clustering aims at generating dendrograms for the rows and columns of the input data matrix. The limitation of using simple hierarchical co-clustering for document clustering is that it has a lot of feature terms and documents, and it also ignores the semantic relations between feature terms. In this paper a semi-supervised clustering algorithm is proposed for hierarchical co-clustering. In the first step feature terms are clustered using a little supervised information. In the second step, the feature terms are merged as new feature attributes. And in the last step, the documents and merged feature terms are clustered using hierarchical co-clustering algorithm. Semantic information is used to measure the similarity during the hierarchical co-clustering process. Experimental results show that the proposed algorithm is effective and efficient.
This work is partially supported by the National Science Foundation of China (Nos. 61170111, 61003142 and 61152001) and the Fundamental Research Funds for the Central Universities (No. SWJTU11ZT08).
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Huang, F., Yang, Y., Li, T., Zhang, J., Rutayisire, T., Mahmood, A. (2012). Semi-supervised Hierarchical Co-clustering. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_39
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DOI: https://doi.org/10.1007/978-3-642-31900-6_39
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