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 ?
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)
In case someone is searching the same for strings with a custom metric
def metric(x, y):
return yourDistFunc(string_seqs[int(x[0])],string_seqs[int(y[0])])
def clusterPockets():
global string_seqs
string_seqs = load_data() #["foo","bar"...]
dat = np.arange(len(string_seqs)).reshape(-1, 1)
clustered_dataset = DBSCAN(metric=metric)).fit(X=dat, y=dat)