Reasonable properties for the ordering of fuzzy quantities (I)
Fuzzy Sets and Systems
Reasonable properties for the ordering of fuzzy quantities (II)
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
| Hi-index | 0.00 |
Clustering methods provide an useful tool to tackle the problem of exploring large-dimensional data. However many common approaches suffer from being applied in high-dimensional spaces. Building on a dissimilarity-based representation of data, we propose a dimensionality reduction technique which preserves the clustering structure of the data. The technique is designed for cases in which data dimensionality is large compared to the number of available observations. In these cases, we represent data in the space of soft D-ranks, by applying the concept of fuzzy ranking. A clustering procedure is then applied. Experimental results show that the method is able to retain the necessary information, while considerably reducing dimensionality.