Soft-ECM: An extension of Evidential C-Means for complex data
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| Title: | Soft-ECM: An extension of Evidential C-Means for complex data |
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| Authors: | Soubeiga, Armel, Guyet, Thomas, Antoine, Violaine |
| Contributors: | Guyet, Thomas |
| Source: | 2025 IEEE International Conference on Fuzzy Systems (FUZZ). :1-6 |
| Publication Status: | Preprint |
| Publisher Information: | IEEE, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, FOS: Computer and information sciences, [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], Artificial Intelligence (cs.AI), Discrete Mathematics (cs.DM), [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Artificial Intelligence, Discrete Mathematics, Machine Learning (cs.LG) |
| Description: | Clustering based on belief functions has been gaining increasing attention in the machine learning community due to its ability to effectively represent uncertainty and/or imprecision. However, none of the existing algorithms can be applied to complex data, such as mixed data (numerical and categorical) or non-tabular data like time series. Indeed, these types of data are, in general, not represented in a Euclidean space and the aforementioned algorithms make use of the properties of such spaces, in particular for the construction of barycenters. In this paper, we reformulate the Evidential C-Means (ECM) problem for clustering complex data. We propose a new algorithm, Soft-ECM, which consistently positions the centroids of imprecise clusters requiring only a semi-metric. Our experiments show that Soft-ECM present results comparable to conventional fuzzy clustering approaches on numerical data, and we demonstrate its ability to handle mixed data and its benefits when combining fuzzy clustering with semi-metrics such as DTW for time series data. |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1109/fuzz62266.2025.11152191 |
| DOI: | 10.48550/arxiv.2507.13417 |
| Access URL: | http://arxiv.org/abs/2507.13417 https://inria.hal.science/hal-05162452v1 |
| Rights: | STM Policy #29 arXiv Non-Exclusive Distribution CC BY |
| Accession Number: | edsair.doi.dedup.....d7d8c83c895a4b75fd91c01fb53c402b |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Clustering based on belief functions has been gaining increasing attention in the machine learning community due to its ability to effectively represent uncertainty and/or imprecision. However, none of the existing algorithms can be applied to complex data, such as mixed data (numerical and categorical) or non-tabular data like time series. Indeed, these types of data are, in general, not represented in a Euclidean space and the aforementioned algorithms make use of the properties of such spaces, in particular for the construction of barycenters. In this paper, we reformulate the Evidential C-Means (ECM) problem for clustering complex data. We propose a new algorithm, Soft-ECM, which consistently positions the centroids of imprecise clusters requiring only a semi-metric. Our experiments show that Soft-ECM present results comparable to conventional fuzzy clustering approaches on numerical data, and we demonstrate its ability to handle mixed data and its benefits when combining fuzzy clustering with semi-metrics such as DTW for time series data. |
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| DOI: | 10.1109/fuzz62266.2025.11152191 |