Editing Talk:2731: K-Means Clustering

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There are two types of ''people'' in the world: those ''who'' use the word “who” to refer to people and the word “that” to refer to things, and those ''who'' don’t. [[Special:Contributions/172.71.151.77|172.71.151.77]] 02:58, 31 January 2023 (UTC)
 
There are two types of ''people'' in the world: those ''who'' use the word “who” to refer to people and the word “that” to refer to things, and those ''who'' don’t. [[Special:Contributions/172.71.151.77|172.71.151.77]] 02:58, 31 January 2023 (UTC)
 
:...and those whom use "whom"..? [[Special:Contributions/172.70.162.57|172.70.162.57]] 09:00, 31 January 2023 (UTC)
 
:...and those whom use "whom"..? [[Special:Contributions/172.70.162.57|172.70.162.57]] 09:00, 31 January 2023 (UTC)
::Sure, there are plenty who misuse “whom” also. “Who / he / she / they VERB” vs “PREPOSITION whom / him / her / them” - who did, who has, who owns, he did, she has, they own - for whom, by whom, about whom, for him, by her, about them. A person who, a thing that. It’s really not that complicated. [[Special:Contributions/172.71.147.21|172.71.147.21]] 10:48, 1 February 2023 (UTC)
 
  
 
Methodological note: k-means is a special case of parametric model-based clustering (here spheres with equal variance) which allows to calculate cluster models with different number of clusters and choose the 'best' one according to the best BIC (Bayesian Information Criterion), see https://cran.r-project.org/package=mclust. A broader non-parametric class of cluster solutions can be fitted with the truecluster meta-algorithm and then choose the one with the best CIC (Cluster Information Criterion), see https://arxiv.org/abs/cs/0601001 and https://arxiv.org/abs/0705.4302. [[User:Joehl|Joehl]] ([[User talk:Joehl|talk]]) 16:55, 2 February 2023 (UTC)
 
Methodological note: k-means is a special case of parametric model-based clustering (here spheres with equal variance) which allows to calculate cluster models with different number of clusters and choose the 'best' one according to the best BIC (Bayesian Information Criterion), see https://cran.r-project.org/package=mclust. A broader non-parametric class of cluster solutions can be fitted with the truecluster meta-algorithm and then choose the one with the best CIC (Cluster Information Criterion), see https://arxiv.org/abs/cs/0601001 and https://arxiv.org/abs/0705.4302. [[User:Joehl|Joehl]] ([[User talk:Joehl|talk]]) 16:55, 2 February 2023 (UTC)

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