Convex fuzzy k-medoids clustering
| Autor: | Daniel Nobre Pinheiro, Simon J. Blanchard, Daniel Aloise |
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| Rok vydání: | 2020 |
| Předmět: |
Hyperparameter
0209 industrial biotechnology Fuzzy clustering Computational complexity theory Logic business.industry Initialization Pattern recognition 02 engineering and technology Fuzzy logic Medoid ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Heuristics business Cluster analysis Mathematics |
| Zdroj: | Fuzzy Sets and Systems. 389:66-92 |
| ISSN: | 0165-0114 |
| DOI: | 10.1016/j.fss.2020.01.001 |
| Popis: | K-medoids clustering is among the most popular methods for cluster analysis despite its use requiring several assumptions about the nature of the latent clusters. In this paper, we introduce the Convex Fuzzy k-Medoids (CFKM) model, which not only relaxes the assumption that objects must be assigned entirely to one and only one medoid, but also that medoids must be assigned entirely to one and only one cluster. The resulting model is convex, thus its resolution is completely robust to initialization. To illustrate the usefulness of the CFKM model, we compare it with two fuzzy k-medoids clustering models: the Fuzzy k-Medoids (FKM) and the Fuzzy Clustering with Multi-Medoids (FMMdd), both solved approximately by heuristics because of their hard computational complexity. Our experiments with both synthetic and real-world data as well as a user survey reveal that the model is not only more robust to the choice hyperparameters of the fuzzy clustering task, but also that it can uniquely discover important aspects of data inherently fuzzy in nature. |
| Databáze: | OpenAIRE |
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