Clustering method based on iterations of neural networks
US10810490B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 8, 2016 |
| Grant date | Oct 20, 2020 |
| Priority date | — |
| Expiry date | Aug 23, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0499
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention relates to a clustering method based on iterations of neural networks, which comprises the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to the number of clusters, each sample representing one cluster, forming an initial exemplar set and training the extreme learning machine; step 3, using current extreme learning machine to cluster samples, which generates a clustering result; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from step 4; and step 6, going back to step 3 to do iteration, otherwise obtaining and outputting clustering result until clustering result is steady or a maximal limit of the number of iterations is reached. The present invention resolves problems that how to realize clustering of high dimensional and nonlinear data space and that the prior art consumes a larger memory or need longer running time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.