# Find threshold in large dataset

I have a problem. I have a data set with some users and their ratings in several movies. The movies are separated into 19 genres.

I want to cluster the users by their preferences(ratings in the movies). The problem is, that I want to find a $threshold ( θ )$ to do the clustering, but I do not know how to do this, because the data are discrete and I cannot use the statistics methods that I know. The threshold is the maximum distance that two users can have to be in the same cluster, like 2 users that likes the same genre movies or have little differences in their tastes.

I've tried to find a threshold using simple statistics. For example, for a user sum all of his ratings in a genre and divide the result via the number of ratings and find some means in some genres, but I didn't got an answer.

Note: I must use BSAS

• It is not clear from your question what threshold means in this context. Please change your question to give more context. Dec 22 '17 at 9:28
• Maybe you should consider usual clustering technics such as agglomerative clustering. With these approaches, you let the algorithm search itself for hidden structure. You then get a dendogramm where you can choose the aggregation threshold or the number of clusters. Here is a summary of this approach : sthda.com/english/articles/… Also, variables you've created with sum of ratings per genre seem good features for a custering. Dec 25 '17 at 8:56