How to use vectors produced by TF-IDF as an input for fuzzy c-means?
$begingroup$
I have done text processing with TF-IDF method and as an output got a list of normalized vectors [0, 1] for each document. Such as below:
Document 1
word1:1.0, word2:0.9, ..., word_n:0
Document 2
word2:1.0, word1:0.4, ..., word_n:0
...
etc
The above is basically a list of key-values where key is a term and values are TF-IDF values, where value 1 means that the term matches the document the most compared to other terms in the set.
My question is, to what form should I transform these vectors in order to properly use fuzzy c-means clustering on them? I feel like it should be 2D matrix of something, but can't figure it out.
At the very end I would like to have a trained model which on a given input could say to what documents (based on the membership values) it belongs with the highest chance.
clustering tfidf
asked 2 days ago
d.dizhevskyd.dizhevsky
1
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$begingroup$
I have done text processing with TF-IDF method and as an output got a list of normalized vectors [0, 1] for each document. Such as below:
Document 1
word1:1.0, word2:0.9, ..., word_n:0
Document 2
word2:1.0, word1:0.4, ..., word_n:0
...
etc
The above is basically a list of key-values where key is a term and values are TF-IDF values, where value 1 means that the term matches the document the most compared to other terms in the set.
My question is, to what form should I transform these vectors in order to properly use fuzzy c-means clustering on them? I feel like it should be 2D matrix of something, but can't figure it out.
At the very end I would like to have a trained model which on a given input could say to what documents (based on the membership values) it belongs with the highest chance.
clustering tfidf
asked 2 days ago
d.dizhevskyd.dizhevsky
1
New contributor
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have done text processing with TF-IDF method and as an output got a list of normalized vectors [0, 1] for each document. Such as below:
Document 1
word1:1.0, word2:0.9, ..., word_n:0
Document 2
word2:1.0, word1:0.4, ..., word_n:0
...
etc
The above is basically a list of key-values where key is a term and values are TF-IDF values, where value 1 means that the term matches the document the most compared to other terms in the set.
My question is, to what form should I transform these vectors in order to properly use fuzzy c-means clustering on them? I feel like it should be 2D matrix of something, but can't figure it out.
At the very end I would like to have a trained model which on a given input could say to what documents (based on the membership values) it belongs with the highest chance.
clustering tfidf
asked 2 days ago
d.dizhevskyd.dizhevsky
1
New contributor
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have done text processing with TF-IDF method and as an output got a list of normalized vectors [0, 1] for each document. Such as below:
Document 1
word1:1.0, word2:0.9, ..., word_n:0
Document 2
word2:1.0, word1:0.4, ..., word_n:0
...
etc
The above is basically a list of key-values where key is a term and values are TF-IDF values, where value 1 means that the term matches the document the most compared to other terms in the set.
My question is, to what form should I transform these vectors in order to properly use fuzzy c-means clustering on them? I feel like it should be 2D matrix of something, but can't figure it out.
At the very end I would like to have a trained model which on a given input could say to what documents (based on the membership values) it belongs with the highest chance.
clustering tfidf
clustering tfidf
asked 2 days ago
d.dizhevskyd.dizhevsky
1
New contributor
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
d.dizhevskyd.dizhevsky
1
New contributor
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
d.dizhevskyd.dizhevsky
1
New contributor
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 2 days ago
d.dizhevskyd.dizhevsky
1
asked 2 days ago
d.dizhevskyd.dizhevsky
1
1
New contributor
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
d.dizhevsky is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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2 Answers
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$begingroup$
Assign a unique column to each word.
All remaining values are zero, obviously.
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
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add a comment |
$begingroup$
You could create a table from the TF-IDF vectors in which each feature or column represents a word, each row a document and if a word does not appear in a document use 0 as TF-IDF vector value. Then you could apply c-means clustering to this table.
answered 8 hours ago
Franziska W.Franziska W.
8114
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Assign a unique column to each word.
All remaining values are zero, obviously.
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
$endgroup$
add a comment |
$begingroup$
Assign a unique column to each word.
All remaining values are zero, obviously.
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
$endgroup$
add a comment |
$begingroup$
Assign a unique column to each word.
All remaining values are zero, obviously.
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
$endgroup$
Assign a unique column to each word.
All remaining values are zero, obviously.
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
answered 8 hours ago
Anony-MousseAnony-Mousse
4,690623
4,690623
add a comment |
add a comment |
$begingroup$
You could create a table from the TF-IDF vectors in which each feature or column represents a word, each row a document and if a word does not appear in a document use 0 as TF-IDF vector value. Then you could apply c-means clustering to this table.
answered 8 hours ago
Franziska W.Franziska W.
8114
$endgroup$
add a comment |
$begingroup$
You could create a table from the TF-IDF vectors in which each feature or column represents a word, each row a document and if a word does not appear in a document use 0 as TF-IDF vector value. Then you could apply c-means clustering to this table.
answered 8 hours ago
Franziska W.Franziska W.
8114
$endgroup$
add a comment |
$begingroup$
You could create a table from the TF-IDF vectors in which each feature or column represents a word, each row a document and if a word does not appear in a document use 0 as TF-IDF vector value. Then you could apply c-means clustering to this table.
answered 8 hours ago
Franziska W.Franziska W.
8114
$endgroup$
You could create a table from the TF-IDF vectors in which each feature or column represents a word, each row a document and if a word does not appear in a document use 0 as TF-IDF vector value. Then you could apply c-means clustering to this table.
answered 8 hours ago
Franziska W.Franziska W.
8114
answered 8 hours ago
Franziska W.Franziska W.
8114
answered 8 hours ago
Franziska W.Franziska W.
8114
answered 8 hours ago
Franziska W.Franziska W.
8114
8114
add a comment |
add a comment |
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