DENsity CLUstering

DENCLUE (DENsity CLUstering) employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points going to the same local maximum are put into the same cluster. DENCLUE works efficiently for high-dimensional data sets and allows arbitrary noise levels while still guaranteeing to find the clustering.

from miml.cluster import DENCLUE

fn = os.path.join(datasets.get_data_home(), 'clustering', 'gaussian',
    'six.txt')
df = DataFrame.read_table(fn, header=None, names=['x1','x2'],
    format='%2f')
x = df.values

model = DENCLUE(1.0, 50)
y = model.fit_predict(x)

scatter(x[:,0], x[:,1], c=y, edgecolor=None, s=3)
title('DENsity CLUstering example')