0511450 20240903203906.620191121235959.9 85075353706 000504747200006 10.1007/s40300-019-00162-5 17 s. P cav_un_epca*03489130026-1424773 (2019)253-270Springer Comparing clusterings Clusters agreement kappa max statistic Normalized mutual information cav_un_auth*0383037 Uglickich Evženie UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0383038 Vlčková D. CZ pdf http://library.utia.cas.cz/separaty/2019/ZS/uglickich-0511450.pdf https://link.springer.com/article/10.1007%2Fs40300-019-00162-5 cav_un_auth*0351997 8A17006 GA MŠk CZ The paper focuses on a problem of comparing clusterings with the same number of clusters obtained as a result of using different clustering algorithms. It proposes a method of the evaluation of the agreement of clusterings based on the combination of the Cohen's kappa statistic and the normalized mutual information. The main contributions of the proposed approach are: (i) the reliable use in practice in the case of a small fixed number of clusters, (ii) the suitability to comparing clusterings with a higher number of clusters in contrast with the original statistics, (iii) the independence on size of the data set and shape of clusters. Results of the experimental validation of the proposed statistic using both simulations and real data sets as well as the comparison with the theoretical counterparts are demonstrated. WOS BB 10000 10100 10103 2020 3 RVO:67985556 http://hdl.handle.net/11104/0302474 S 3+4 Article Mathematics C 0.311 Q4 2019 85075353706 SCOPUS PUBMED 000504747200006 WOS pdf cav_un_epca*0348913 Metron 0026-1424 2281-695X Roč. 77 č. 3 2019 253 270 Springer