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\iteman{io-port 06104183}
\itemau{Zhang, Jinyuan; Yang, Yan; Wang, Hongjun; Mahmood, Amjad; Huang, Feifei}
\itemti{Semi-supervised clustering ensemble based on collaborative training.}
\itemso{Li, Tianrui (ed.) et al., Rough sets and knowledge technology. 7th international conference, RSKT 2012, Chengdu, China, August 17--20, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-31899-3/pbk). Lecture Notes in Computer Science 7414. Lecture Notes in Artificial Intelligence, 450-455 (2012).}
\itemab
Summary: 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.
\itemrv{~}
\itemcc{}
\itemut{Semi-supervised clustering ensemble model; collaborative training; semi-supervised learning; clustering ensemble}
\itemli{doi:10.1007/978-3-642-31900-6\_55}
\end