Quickshift++

This is not an officially supported Google product

Density-based clustering algorithm based on mode-seeking.

Usage

Initializiation:

.. code-block:: python

QuickshiftPP(k, beta)

k: number of neighbors in k-NN

beta: fluctuation parameter which ranges between 0 and 1.

Finding Clusters:

.. code-block:: python

fit(X)

X is the data matrix, where each row is a datapoint in euclidean space.

fit performs the clustering. The final result can be found in QuickshiftPP.memberships.

Example (mixture of two gaussians):

.. code-block:: python

from QuickshiftPP import * import numpy as np

X = [np.random.normal(0, 1, 2) for i in range(100)] + [np.random.normal(5, 1, 2) for i in range(100)] y = [0] * 100 + [1] * 100

Declare a Quickshift++ model with tuning hyperparameters.

model = QuickshiftPP(k=20, beta=.5)

Compute the clustering.

model.fit(X) y_hat = model.memberships

from sklearn.metrics.cluster import adjusted_rand_score, adjusted_mutual_info_score print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y)) print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))

Install

This package uses distutils, which is the default way of installing python modules.

To install for all users on Unix/Linux::

sudo python setup.py build; python setup.py install

Dependencies

python 2.7, scikit-learn