I have a data set where the samples are people and the feature are their age, sex, location, job, height, weight… Then I will have a new person with the same information and my goal is the find the twenty closest persons from him/her.
I think a clustering algorithm on Python could do the job. But there is a lot of different clustering algorithms and I don’t know which one will fit my problem the best.
KMeans seems to be the easiest clustering method but I don’t know the number of clusters. Same problem with Ward hierarchical clustering.
Mean shift seems more advanced and complicated, I haven’t found a lot of documentation on it. Then my data set is about $100 000$ samples and the maximum recommended is $10 000$ for MeanShift but I could easily separate the data set per area to reduce its size.
Do you think a clustering method is a good idea ? Which one should be the best for my problem ?