DBSCAN_multiplex 1.3

Fast and memory-efficient DBSCAN clustering,possibly on various subsamples out of a common dataset

DBSCAN\_multiplex
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Overview
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A fast and memory-efficient implementation of DBSCAN (Density-Based
Spatial Clustering of Applications with Noise).

It is especially suited for multiple rounds of down-sampling and
clustering from a joint dataset: after an initial overhead O(N log(N)),
each subsequent run of clustering will have O(N) time complexity.

As illustrated by a doctest embedded in the present module's docstring,
on a dataset of 15,000 samples and 47 features, DBSCAN\_multiplex
processes 50 rounds of sub-sampling and clustering in about 4 minutes,
whereas Scikit-learn's implementation of DBSCAN performs the same task
in more than 28 minutes.

Such a test can be performed quite conveniently on your machine: simply
entering ``python DBSCAN_multiplex``\ in your terminal will prompt a
doctest to start comparing the performance of the two afore-mentioned
implementations of DBSCAN.

This 7-fold gain in performance proved critical to the statistical
learning application that prompted the design of this algorithm.

Installation and Requirements
-----------------------------

DBSCAN\_multiplex requires Python 2.7 along with the following packages
and a few modules from the Standard Python Library: \* NumPy >= 1.9 \*
PyTables \* scikit-learn

Upon checking that the required dependencies are installed, you can
upload DBSCAN\_multiplex from the official Python Package Index (PyPI)
as follows: \* open a terminal window; \* type the command:
``pip install DBSCAN_multiplex``

Usage and Example
-----------------

See the docstrings associated to each function of the DBSCAN\_multiplex
module for more information; in a Python interpreter console, they can
be viewed by calling the built-in help system, e.g.,
``help(DBSCAN_multiplex.load)``.

The following few lines show how DBSCAN\_multiplex can be used for
clustering 50 randomly selected subsamples out of a common Gaussian
distributed dataset. This situation arises in consensus clustering where
one might want to obtain and then combine multiple vectors of cluster
labels.

::

>>> import numpy as np
>>> import DBSCAN_multiplex as DB

>>> data = np.random.randn(15000, 7)
>>> N_iterations = 50
>>> N_sub = 9 * data.shape[0] / 10
>>> subsamples_matrix = np.zeros((N_iterations, N_sub), dtype = int)
>>> for i in xrange(N_iterations): subsamples_matrix[i] = np.random.choice(data.shape[0], N_sub, replace = False)
>>> eps, labels_matrix = DB.DBSCAN(data, minPts = 3, subsamples_matrix = subsamples_matrix, verbose = True)