@inproceedings{dd015683504f4493a0ca90a22bb73cdc,
title = "A new ensemble clustering method based on dempster-shafer evidence theory and gaussian mixture modeling",
abstract = "This paper proposes a new method based on Dempster-Shafer (DS) evidence theory and Gaussian Mixture Modeling (GMM) technique to combine the cluster results from single clustering methods. We introduce the GMM technique to determine the confidence values for candidate results from each clustering method. Then we employ the DS theory to combine the evidences supplied by different clustering methods, based on which the final result is obtained. We tested the proposed ensemble clustering method on several commonly used datasets. The experimental results confirm that our method is effective and promising.",
keywords = "Data clustering, Dempster-Shafer (DS) evidence theory, Ensemble clustering, Gaussian Mixture Modeling (GMM)",
author = "Y. Wu and Xiabi Liu and Lunhao Guo",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 21st International Conference on Neural Information Processing, ICONIP 2014 ; Conference date: 03-11-2014 Through 06-11-2014",
year = "2014",
doi = "10.1007/978-3-319-12640-1\_1",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1--8",
editor = "Loo, \{Chu Kiong\} and Yap, \{Keem Siah\} and Wong, \{Kok Wai\} and Andrew Teoh and Kaizhu Huang",
booktitle = "Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings",
address = "Germany",
}