DBSCAN\_multiplex
=================
Overview
--------
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, on a Asus Zenbook laptop
with 8 GiB of RAM and an Intel Core M processor,
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 a machine running any member of the Unix-like
family of operaating systems, Python 2.7 along with the following packages
and a few modules from the Standard Python Library:
- NumPy >= 1.9
- PyTables
- scikit-learn
You can install DBSCAN_multiplex from the official Python Package Index (PyPI) as follows:
- open a terminal window;
- type in ``pip install DBSCAN_multiplex``.
The command listed above should automatically install or upgrade any missing or outdated dependency among those listed at the beginning of this section.
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)
References
----------
- Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996) "A density-based
algorithm for discovering clusters in large spatial databases with noise".
Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining (KDD-96). AAAI Press. pp. 226-231.
- Kriegel, H.-P., Kroeger, P., Sander, J. and Zimek, A. (2011) "Density-based
Clustering". WIREs Data Mining and Knowledge Discovery 1 (3): 231-240.
