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I have large set of data records looking like this:

"text", "category"

I extract n-grams from text (2-, 3- and 4-grams) and store count of each n-gram per category, like so:

"ngram1", "category1", 1000
"ngram1", "category2", 20
"ngram1", "category3", 15
"ngram2", "category1", 25
"ngram2", "category2", 550
"ngram2", "category3", 600

Is there a clustering or classification algorithm that could help with finding groups within categories, based on counts of same n-grams within categories?

In the above example that algorithm should be able to show that "category2" and "category3" are part of the same group.

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    $\begingroup$ You can represent each category as a vector of ngram counts: category1 = [1000 25 ...]. After that you can apply your clustering algorithm of choice. $\endgroup$ – Emre May 8 '17 at 18:24
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If your ultimate goal is to cluster similar categories and assuming that you have labels of each text as category1, category2,...,categoryN from 1 to N, a bag of words method would be sufficient in order to create features so that you can run multiple desired clustering algorithms.

K-means can be a good starting point for getting similar groups of text document categories, you can try out different k for more sensible outcomes.

Methodology that I would adapt here, forgive me if understood your problem wrong, would be to concatenate all the textual data for a given category and create 1,2,3,..n gram vectors. The values of these vectors can be TF-IDF of a given n-gram, and so on. Still different feature matrices can be formed to compare category vectors but TF-IDF is a pretty common way to do it in.

After acquiring n-gram feature vectors for each text category, you can apply k-means algorithm with several distance metrics to find similar categories.

Suppose we have 5 categories of text as given:

  1. Soccer Games
  2. Basketball Games
  3. Politics
  4. Movies

The proposed method can capture the similarity between soccer and basketball games as they are both sports.

Hope this can help

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Looking at the problem statement, it seems all you want is to apply Hierarchical clustering algorithm to map sub categories with their category(child-parent relationship).

Clustering algorithm provides you the distance of each cluster with near by cluster and intra-distance within the cluster. This could not probably solve your problem. Go for Hierarchical clustering to map sub-categories with categories.

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