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I have a dataset that has the opinions of 30 different TV shows for 2000 high school students.

A student could have said they liked the show, did not have an opinion, or disliked the show. These values then translate to [1,0,-1] in my dataset.

I would now like to find approx 10-20 clusters of student types based on this dataset.

Because there are only 3 unique values in each field, it seems like this data should follow clustering rules for binary data (ex: avoid kmeans). I have been reading literature on that but am still struggling to determine what the best methods would be.

If anyone has any input on what would be best, I would really appreciate it!

Below is smaller example of the dataset:

Student Friends Seinfeld HIMYM Cheers
Student A 1 1 1 -1
Student B 1 0 0 -1
Student C 0 1 -1 -1
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  • $\begingroup$ why don't you want to use kmeans? $\endgroup$
    – alexmolas
    Commented Jul 21, 2022 at 8:16

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I don't think there is a "best" method in clustering. Here are some ideas (all use k-means in the end though):

  1. Read this Netflix price paper and try to use matrix decomposition to assign each student a vector of small dimension, then cluster these vectors using k-means.

  2. Choose a random matrix of dimensions d x 30, with row vectors standardized to length one, and d small. Multiply with your dataset. Try to cluster the resulting 2000 points of dimension d using k-means.

  3. Or just use k-means because dimension 30 is not that large :).

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