google / quickshift

A clustering algorithm that first finds the high-density regions (cluster-cores) of the data and then clusters the remaining points by hill-climbing. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by popular algorithm such as mean shift. (https://arxiv.org/abs/1805.07909)

Quickshift++ ====== This is not an officially supported Google product

Density-based clustering algorithm based on mode-seeking.

Usage

Initializiation:

QuickshiftPP(k, beta) 

k: number of neighbors in k-NN

beta: fluctuation parameter which ranges between 0 and 1.

Finding Clusters:

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):

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

quickshift's Issues

Quick shift modal regression

Hi Heinrich - Was reading your paper "On the Consistency of Quick Shift" and was hoping you might be able to share some code with respect to the modal regression. I'd like to see if how it works with multi-output regression. Any chance you have anything available? Thanks much!