Min-Max-Jump distance and its applications
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arXiv
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911939371728896 |
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| author | Liu, Gangli |
| author_facet | Liu, Gangli |
| contents | We explore three applications of Min-Max-Jump distance (MMJ distance). MMJ-based K-means revises K-means with MMJ distance. MMJ-based Silhouette coefficient revises Silhouette coefficient with MMJ distance. We also tested the Clustering with Neural Network and Index (CNNI) model with MMJ-based Silhouette coefficient. In the last application, we tested using Min-Max-Jump distance for predicting labels of new points, after a clustering analysis of data. Result shows Min-Max-Jump distance achieves good performances in all the three proposed applications. In addition, we devise several algorithms for calculating or estimating the distance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_05994 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Min-Max-Jump distance and its applications Liu, Gangli Computer Vision and Pattern Recognition We explore three applications of Min-Max-Jump distance (MMJ distance). MMJ-based K-means revises K-means with MMJ distance. MMJ-based Silhouette coefficient revises Silhouette coefficient with MMJ distance. We also tested the Clustering with Neural Network and Index (CNNI) model with MMJ-based Silhouette coefficient. In the last application, we tested using Min-Max-Jump distance for predicting labels of new points, after a clustering analysis of data. Result shows Min-Max-Jump distance achieves good performances in all the three proposed applications. In addition, we devise several algorithms for calculating or estimating the distance. |
| title | Min-Max-Jump distance and its applications |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2301.05994 |