DBSCAN with custom metric

Question

I have the following given:

• a dataset in the range of thousands

• a way of computing the similarity, but the datapoints themselves I cannot plot them in euclidian space

I know that DBSCAN should support custom distance metric but I dont know how to use it.

say I have a function

def similarity(x,y):
    return  similarity ... 

and I have a list of data that can be passed pairwise into that function, how do I specify this when using the DBSCAN implementation of scikit-learn ?

Ideally what I want to do is to get a list of the clusters but I cant figure out how to get started in the first place.

There is a lot of terminology that still confuses me:

http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html

How do I pass a feature array and what is it ? How do I fit this implementation to my needs ? How will I be able to get my "sublists" from this algorithm ?

Answer 1

A "feature array" is simply an array of the features of a datapoint in your dataset.

metric is the parameter you're looking for. It can be a string (the name of a builtin metric), or a callable. Your similarity function is a callable. This isn't well described in the documentation, but a metric has to do just that, take two datapoints as parameters, and return a number.

def similarity(x, y):
    return ...

reduced_dataset = sklearn.cluster.DBSCAN(metric=similarity).fit(dataset)