Data Summarisation by Typicality-based Clustering for Vectorial and Non Vectorial Data
| Type of publication: | Inproceedings |
| Citation: | lesot2006summarisation |
| Journal: | Fuzzy Systems, 2006 IEEE International Conference on |
| Booktitle: | Fuzzy Systems, 2006 IEEE International Conference on |
| Year: | 2006 |
| Pages: | 547--554 |
| DOI: | 10.1109/fuzzy.2006.1681765 |
| Abstract: | In this paper, a typicality-based clustering algorithm is proposed: it exploits typicality degrees defined in a prototype construction framework to identify a decomposition of the dataset into homogeneous and distinct clusters and to provide characteristic representatives of the obtained clusters, so as to summarise the initial dataset. The proposed algorithm can be applied both to vectorial and non vectorial data, such as trees for instance. Tests performed on artificial and real data illustrate the interest of the proposed approach. |
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| Authors | |
| Added by: | [GR] |
| Total mark: | 0 |
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