Constraining Shape and Size in Clustering
| Type of publication: | Incollection |
| Citation: | borgelt2009constraining |
| Booktitle: | Cooperation in Classification and Data Analysis |
| Year: | 2009 |
| Pages: | 13--25 |
| Publisher: | Springer |
| Address: | Berlin |
| URL: | http://www.springerlink.com/co... |
| DOI: | 10.1007/978-3-642-00668-5_2 |
| Abstract: | Several of the more sophisticated fuzzy clustering algorithms, like the Gustafson-Kessel algorithm and the fuzzy maximum likelihood estimation (FMLE) algorithm, offer the possibility to induce clusters of ellipsoidal shape and differing sizes. The same holds for the expectation maximization (EM) algorithm for a mixture of Gaussian distributions. However, these additional degrees of freedom can reduce the robustness of the algorithms, thus sometimes rendering their application problematic, since results are unstable. In this paper we suggest methods to introduce shape and size constraints that handle this problem effectively. |
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| Added by: | [GR] |
| Total mark: | 0 |
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