Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study
Open Access
- 17 March 2022
- journal article
- research article
- Published by Springer Science and Business Media LLC in Advances in Data Analysis and Classification
- Vol. 17 (1), 211-238
- https://doi.org/10.1007/s11634-022-00496-5
Abstract
No abstract availableKeywords
Funding Information
- Bundesministerium für Bildung und Forschung (01IS18036A)
This publication has 32 references indexed in Scilit:
- A Plea for Neutral Comparison Studies in Computational SciencesPLOS ONE, 2013
- The self‐assessment trap: can we all be better than average?Molecular Systems Biology, 2011
- Over-optimism in bioinformatics: an illustrationBioinformatics, 2010
- Reporting bias when using real data sets to analyze classification performanceBioinformatics, 2009
- Characterization and evaluation of similarity measures for pairs of clusteringsKnowledge and Information Systems, 2008
- On Similarity Indices and Correction for Chance AgreementJournal of Classification, 2006
- Robust clustering methods: a unified viewIEEE Transactions on Fuzzy Systems, 1997
- Comparing partitionsJournal of Classification, 1985
- Least squares quantization in PCMIEEE Transactions on Information Theory, 1982
- The estimation of the gradient of a density function, with applications in pattern recognitionIEEE Transactions on Information Theory, 1975