BCubed Revisited: Elements Like Me
| Authors | |
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| Publication date | 2022 |
| Book title | ICTIR'22 |
| Book subtitle | proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval : July 11-12, 2022, Madrid, Spain |
| ISBN (electronic) |
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| Event | 8th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2022 |
| Pages (from-to) | 127-132 |
| Publisher | New York, NY: The Association for Computing Machinery |
| Organisations |
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| Abstract |
BCubed is a mathematically clean, elegant and intuitively well behaved external performance metric for clustering tasks. BCubed compares a predicted clustering to a known ground truth through elementwise precision and recall scores. For each element, the predicted and ground truth clusters containing the element are compared, and the mean over all elements is taken. We argue that BCubed overestimates performance, for the intuitive reason that the clustering gets credit for putting an element in its own cluster. This is repaired, and we investigate the repaired version, called "Elements Like Me (ELM)". We extensively evaluate ELM and conclude that it retains all positive properties of BCubed and gives a minimum 0 zero score when it should.
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| Document type | Conference contribution |
| Language | English |
| Related dataset | BCubed Revisited: Elements Like Me Dataset |
| Published at | https://doi.org/10.1145/3539813.3545121 |
| Downloads |
3539813.3545121
(Final published version)
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| Permalink to this page | |
