Normalized Mutual Information (NMI) and B3 are used for extrinsic clustering evaluation metrics when each instance (sample) has only one label.
What are equivalent metrics when each instance (sample) has only one label?
For example, in first image, we see [apple, orange, pears], in second image, we see [orange, lime, lemon] and in third image, we see [apple], and in the forth image we see [orange]. Then, if put first image and last image in the one cluster it is good, and if put third and forth image in one cluster is bad.
Application: Many popular datasets for object detection or image segmentation have multi labels for each image. If we used this data for classification (not detection and not segmentation), we have multiple labels for each image.
Note: My task is unsupervised clustering, not supervised classification. I know that for supervised classification, we can use top-5 or top-10 score. But I do not know what will be in unsupervised clustering.
Dear Mohsen,
Have a look at http://www.mit.edu/~9.54/fall14/slides/Class13.pdf
Thanks André M Carrington for answer. Using nary similarity and distance measures are goog. I found following papers that are better because they use possibilistic instead of binary.
Anderson, D. T., Bezdek, J. C., Popescu, M., & Keller, J. M. (2010). Comparing fuzzy, probabilistic, and possibilistic partitions. IEEE Transactions on Fuzzy Systems, 18(5), 906-918.
Jenhani, I., Benferhat, S., & Elouedi, Z. (2010). Possibilistic similarity measures. In Foundations of Reasoning under Uncertainty (pp. 99-123). Springer, Berlin, Heidelberg.
Lei, Y., Bezdek, J. C., Chan, J., Vinh, N. X., Romano, S., & Bailey, J. (2016). Extending information-theoretic validity indices for fuzzy clustering. IEEE Transactions on Fuzzy Systems, 25(4), 1013-1018.
- Badges
- Science topic