Large-sample results for optimization-based clustering methods
β Scribed by Peter G. Bryant
- Book ID
- 110592258
- Publisher
- Springer
- Year
- 1991
- Tongue
- English
- Weight
- 646 KB
- Volume
- 8
- Category
- Article
- ISSN
- 0176-4268
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
As known, the clustering data obtained by the paired comparisons or questionnaires are symmetric and can be represented by a fuzzy symmetric and reflexive matrix B which is called to a fuzzy similarity matrix in this paper. In general, they do not necessarily satisfy the fuzzy transitive condition w
In many support vector-based clustering algorithms, a key computational bottleneck is the cluster labeling time of each data point which restricts the scalability of the method. In this paper, we review a general framework of support vector-based clustering using dynamical system and propose a novel