Timeline for Normalization/Standarization for Clustering visualization
Current License: CC BY-SA 3.0
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| when toggle format | what | by | license | comment | |
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| Jul 28, 2016 at 0:17 | history | edited | bones.felipe | CC BY-SA 3.0 |
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| Jul 26, 2016 at 0:20 | answer | added | Has QUIT--Anony-Mousse | timeline score: 1 | |
| Jul 24, 2016 at 23:57 | history | edited | bones.felipe | CC BY-SA 3.0 |
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| Jul 24, 2016 at 23:56 | comment | added | bones.felipe | Not exactly, I am not doing only the term frequency matrix but adding Inverse document frequency such that kind of effect you are mentioning is neutralized, but indeed at some point the size of the documents impose some bias. | |
| Jul 24, 2016 at 22:01 | comment | added | roundsquare | I see. Why is that your measure of importance? It basically means that documents with more words are more important... is that really true for what you are doing? I.e. with a term-frequency matrix, the "importance" is the number of words in each document. This seems problematic since there are often cluster that pick up "small" documents i.e. whose vector is closer to the origin and others that pick up "large" documents i.e. those further from origin. I think something like the number of documents would scale better and give you a better idea of the importance of a cluster. | |
| Jul 24, 2016 at 3:53 | history | edited | bones.felipe | CC BY-SA 3.0 |
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| Jul 24, 2016 at 3:26 | comment | added | bones.felipe | Its the "importance" of the cluster, as every document is a vector I take the sum of every vector in a cluster and then sum all the components, that gaves a sense of the cluster "importance" that is what I call the weight. | |
| Jul 24, 2016 at 2:58 | comment | added | roundsquare | What do you mean by the "weight for each cluster"? | |
| Jul 24, 2016 at 1:11 | review | First posts | |||
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| Jul 24, 2016 at 1:10 | history | asked | bones.felipe | CC BY-SA 3.0 |