Talk:Consensus clustering: Difference between revisions

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Needs expansion

Please, more reference and expand description of the alghorithms. 00:55, 7 October 2010 178.73.63.95 (talk)

Yes, effectively no information here. Melcombe (talk) 16:46, 13 December 2010 (UTC)[reply]

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Problems with the PAC score

I noticed the author of the PAC score paper has inserted a section on their method with graphics on this page. I think this is fine, as it is an important addition to the Monti consensus clustering method (which I have now added mathematical detail on), although I did reduce the detail on this one paper a bit, because I thought it was over glorified. We have found in practice the PAC score works poorly because it overestimates K all the time, in the scientific reports paper the method was not tested on real data, instead just simulations were used. We have fixed this method by adding a Monte Carlo simulation to the Monti consensus clustering algorithm (M3C). I added a brief few sentences to explain this method. However, there is a wikipedia user 'HelpUsStopSpam' who thought this section should be deleted because I am the primary author of the paper. I really do think a new comment should be added at somepoint to explain the inherant bias of the Monti consensus clustering algorithm and the PAC score. A sentence or two is all that is required. Basically, as K increases so does the stability of the consensus matrix on random data, this is a major problem we have noticed again and again on cancer and other datasets. Thank you.

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 ==Needs expansion==
 Please, more reference and expand description of the alghorithms. 00:55, 7 October 2010 178.73.63.95 (talk)
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 == Problems with the PAC score ==
-I noticed the author of the PAC score paper has inserted a large section on their method with graphics on this page. I think this is fine, as it is an important addition to the Monti consensus clustering method (which I have now added mathematical detail on). I did reduce the detail on this one paper a bit, because I thought it was over glorified. However, we have found in practice the PAC score works poorly because it overestimates K all the time, in the scientific reports paper the method was not tested on real data, instead just simulations were used. We have fixed this method by adding a Monte Carlo simulation to the Monti consensus clustering algorithm (M3C). I added a brief few sentences to explain this method. However, there is a wikipedia user 'HelpUsStopSpam' who thought this section should be deleted because I am the primary author of the paper. I really do think a new comment should be added at somepoint to explain the inherant bias of the Monti consensus clustering algorithm and the PAC score. Basically, as K increases so does the stability of the consensus matrix on random data, this is a major problem we have noticed again and again on cancer and other datasets. Thank you.
+I noticed the author of the PAC score paper has inserted a section on their method with graphics on this page. I think this is fine, as it is an important addition to the Monti consensus clustering method (which I have now added mathematical detail on), although I did reduce the detail on this one paper a bit, because I thought it was over glorified. We have found in practice the PAC score works poorly because it overestimates K all the time, in the scientific reports paper the method was not tested on real data, instead just simulations were used. We have fixed this method by adding a Monte Carlo simulation to the Monti consensus clustering algorithm (M3C). I added a brief few sentences to explain this method. However, there is a wikipedia user 'HelpUsStopSpam' who thought this section should be deleted because I am the primary author of the paper. I really do think a new comment should be added at somepoint to explain the inherant bias of the Monti consensus clustering algorithm and the PAC score. A sentence or two is all that is required. Basically, as K increases so does the stability of the consensus matrix on random data, this is a major problem we have noticed again and again on cancer and other datasets. Thank you.