Abstract
Recent researches on data clustering is increasingly focusing on combining multiple data partitions as a way to improve the robustness of clustering solutions. Most of them focused on crisp clustering combination. Semi-supervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. However, in this paper, we offer a semi-supervised clustering ensemble model based on collaborative training (SCET) and an unsupervised clustering ensemble mode based on collaborative training (UCET). In the ensemble step of SCET, semi-supervised learning is introduced. While in UCET, the knowledge used in SCET is replaced by information extracted from the base-clusterings. Then tri-training is used as consensus of clustering ensemble. The experiments on datasets from UCI machine learning repository indicate that the model improves the accuracy of clustering.
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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Zhang, J., Yang, Y., Wang, H., Mahmood, A., Huang, F. (2012). Semi-supervised Clustering Ensemble Based on Collaborative Training. 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_55
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DOI: https://doi.org/10.1007/978-3-642-31900-6_55
Publisher Name: Springer, Berlin, Heidelberg
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