I have to cluster a movie dataset of 10000 movies. A movie has attributes like Genres, Actors, Directors, Year. Earlier I thought that we can use a simple clustering algorithm like k-medoids and the distance can be pre-computed between two movies by subtracting genres & actors.
Initialise d(movie1, movie2) = 0 d(movie1, movie2) -= number of common genres d(movie1, movie2) -= number of common actors d(movie1, movie2) -= 1 (if they have a common director) d(movie1, movie2) -= 1 (if they belong to same decade)
Is this approach correct ? Is k-medoids fast enough to cluster this dataset (I doubt it isn't) ? If it isn't fast enough any better clustering algorithm and strategy to cluster this dataset ?