2D hierarchical fuzzy clustering using kernel-based membership functions
2016
2D clustering aims at solving problems concerning bi-dimensional datasets in several application fields, such as medical imaging, image retrieval, computer vision and so on. A novel approach for 2D hierarchical fuzzy clustering is proposed, which relies on the use of kernel-based membership functions. This new metric allows to obtain unconstrained structures for data modelling. The performed tests show that the proposed approach can overcome well-known hierarchical clustering algorithms against different benchmarks, also having the chance to be deployed on parallel computing architectures.
Keywords:
- Hierarchical clustering
- Fuzzy clustering
- FLAME clustering
- Hierarchical clustering of networks
- Cluster analysis
- Machine learning
- Canopy clustering algorithm
- Brown clustering
- Computer science
- Artificial intelligence
- Pattern recognition
- CURE data clustering algorithm
- Correlation clustering
- Data stream clustering
- Data mining
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