DenMune: A density-peak clustering algorithm

DenMune a clustering algorithm that can find clusters of arbitrary size, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne. The algorithm relies on a single parameter K (the number of nearest neighbors). The results show the superiority of the algorithm. Enjoy the simplicity but the power of DenMune.

PyPI Version Launch notebook examples in Binder Documentation Status Launch notebook examples in Colaboratory, Google Research Launch notebook examples in Kaggle, the workspace where data scientist meet Elsevier, journal's article publisher Research datasets at Mendeley BSD 3-Clause “New” or “Revised” License" CircleCI, continuous integration

Based on the paper

Paper

Journal

Mohamed Abbas, Adel El-Zoghabi, Amin Ahoukry

scimagojr

DenMune: Density peak based clustering using mutual nearest neighbors

In: Journal of Pattern Recognition, Elsevier,

volume 109, number 107589, January 2021

DOI: https://doi.org/10.1016/j.patcog.2020 .107589

Documentation:

Documentation, including tutorials, are available on https://denmune.readthedocs.io

read the documentation

Watch it in action

This 30 seconds will tell you how a density-baased algorithm, DenMune propagates:

interact with the propagation

Propagation in DenMune

When less means more

Most calssic clustering algorithms fail in detecting complex where clusters are of different size, shape, density, and being exist in noisy data. Recently, a density-based algorithm named DenMune showed great ability in detecting complex shapes even in noisy data. it can detect number of clusters automatically, detect both pre-identified-noise and post-identified-noise automatically and removing them.

It can achieve accuracy reach 100% in most classic pattern problems, achieve 97% in MNIST dataset. A great advantage of this algorithm is being single-parameter algorithm. All you need is to set number of k-nearest neighbor and the algorithm will care about the rest. Being Non-senstive to changes in k, make it robust and stable.

Keep in mind, the algorithm reduce any N-D dataset to only 2-D dataset initially, so it is a good benefit of this algorithm is being always to plot your data and explore it which make this algorithm a good candidate for data exploration. Finally, the algorithm comes with neat package for visualizing data, validating it and analyze the whole clustering process.

Installation and Usage

Install It:

Simply install DenMune clustering algorithm using pip command from the official Python repository

PyPI Version

From the shell run the command

pip install denmune

From jupyter notebook cell run the command

!pip install denmune

Import It:

Once DenMune is installed, you just need to import it

from denmune import DenMune

Note

Please note that first denmune (the package) is in small letters, while DenMune (the class itself) has D and M in capital case while other letters are small.

Loading data

There are four possible cases of data:

  • only train data without labels

  • only labeld train data

  • labeled train data in addition to test data without labels

  • labeled train data in addition to labeled test data

#=============================================
# First scenario: train data without labels
# ============================================

data_path = 'datasets/denmune/chameleon/'
dataset = "t7.10k.csv"
data_file = data_path + dataset

# train data without labels
X_train = pd.read_csv(data_file, sep=',', header=None)

knn = 39 # k-nearest neighbor, the only parameter required by the algorithm

dm = DenMune(train_data=X_train, k_nearest=knn)
labels, validity = dm.fit_predict(show_analyzer=False, show_noise=True)

This is an intutive dataset which has no groundtruth provided

t710

#=============================================
# Second scenario: train data with labels
# ============================================

data_path = 'datasets/denmune/shapes/'
dataset = "aggregation.csv"
data_file = data_path + dataset

# train data with labels
X_train = pd.read_csv(data_file, sep=',', header=None)
y_train = X_train.iloc[:, -1]
X_train = X_train.drop(X_train.columns[-1], axis=1)

knn = 6 # k-nearest neighbor, the only parameter required by the algorithm

dm = DenMune(train_data=X_train, train_truth= y_train, k_nearest=knn)
labels, validity = dm.fit_predict(show_analyzer=False, show_noise=True)

Datset groundtruth

aggregation groundtruth

Datset as detected by DenMune at k=6

aggregation train

#=================================================================
# Third scenario: train data with labels in addition to test data
# ================================================================

data_path = 'datasets/denmune/pendigits/'
file_2d = data_path + 'pendigits-2d.csv'

# train data with labels
X_train = pd.read_csv(data_path + 'train.csv', sep=',', header=None)
y_train = X_train.iloc[:, -1]
X_train = X_train.drop(X_train.columns[-1], axis=1)

# test data without labels
X_test = pd.read_csv(data_path + 'test.csv', sep=',', header=None)
X_test = X_test.drop(X_test.columns[-1], axis=1)

knn = 50 # k-nearest neighbor, the only parameter required by the algorithm

dm = DenMune(train_data=X_train, train_truth= y_train,
             test_data= X_test,
             k_nearest=knn)
labels, validity = dm.fit_predict(show_analyzer=True, show_noise=True)

dataset groundtruth

pendigits groundtruth

dataset as detected by DenMune at k=50

pendigits train

test data as predicted by DenMune on training the dataset at k=50

pendigits test

Algorithm’s Parameters

  1. Parameters used within the initialization of the DenMune class

def __init__ (self,
                  train_data=None, test_data=None,
                  train_truth=None, test_truth=None,
                  file_2d ='_temp_2d', k_nearest=10,
                  rgn_tsne=False, prop_step=0,
                  ):

  • train_data:

    • data used for training the algorithm

    • default: None. It should be provided by the use, otherwise an error will riase.

  • train_truth:

    • labels of training data

    • default: None

  • test_data:

    • data used for testing the algorithm

  • test_truth:

    • labels of testing data

    • default: None

  • k_nearest:

    • number of nearest neighbor

    • default: 10. It should be provided by the user.

  • rgn_tsn:

    • when set to True: It will regenerate the reduced 2-D version of the N-D dataset each time the algorithm run.

    • when set to False: It will generate the reduced 2-D version of the N-D dataset first time only, then will reuse the saved exist file

    • default: True

  • file_2d: name (include location) of file used save/load the reduced 2-d version

    • if empty: the algorithm will create temporary file named ‘_temp_2d’

    • default: _temp_2d

  • prop_step:

    • size of increment used in showing the clustering propagation.

    • leave this parameter set to 0, the default value, unless you are willing intentionally to enter the propagation mode.

    • default: 0

  1. Parameters used within the fit_predict function:

def fit_predict(self,
                   validate=True,
                   show_plots=True,
                   show_noise=True,
                   show_analyzer=True
                   ):

  • validate:

    • validate data on/off according to five measures integrated with DenMUne (Accuracy. F1-score, NMI index, AMI index, ARI index)

    • default: True

  • show_plots:

    • show/hide plotting of data

    • default: True

  • show_noise:

    • show/hide noise and outlier

    • default: True

  • show_analyzer:

    • show/hide the analyzer

    • default: True

Features

The Analyzer

The algorithm provide an intutive tool called analyzer, once called it will provide you with in-depth analysis on how your clustering results perform.

DenMune Analyzer

Noise Detection

DenMune detects noise and outlier automatically, no need to any further work from your side.

  • It plots pre-identified noise in black

  • It plots post-identified noise in light grey

You can set show_noise parameter to False.

# let us show noise

m = DenMune(train_data=X_train, k_nearest=knn)
labels, validity = dm.fit_predict(show_noise=True)

# let us show clean data by removing noise

m = DenMune(train_data=X_train, k_nearest=knn)
labels, validity = dm.fit_predict(show_noise=False)

noisy data

clean data

noisy data

clean data

Validatation

You can get your validation results using 3 methods

  • by showing the Analyzer

  • extract values from the validity returned list from fit_predict function

  • extract values from the Analyzer dictionary

There are five validity measures built-in the algorithm, which are:

  • ACC, Accuracy

  • F1 score

  • NMI index (Normalized Mutual Information)

  • AMI index (Adjusted Mutual Information)

  • ARI index (Adjusted Rand Index)

validation snapshot

K-nearest Evolution

The following chart shows the evolution of pre and post identified noise in correspondence to increase of number of knn. Also, detected number of clusters is analyzed in the same chart in relation with both types of identified noise.

knn evolution chart

The Scalability

data size

time

data size: 5000

time: 2.3139 seconds

data size: 10000

time: 5.8752 seconds

data size: 15000

time: 12.4535 seconds

data size: 20000

time: 18.8466 seconds

data size: 25000

time: 28.992 seconds

data size: 30000

time: 39.3166 seconds

data size: 35000

time: 39.4842 seconds

data size: 40000

time: 63.7649 seconds

data size: 45000

time: 73.6828 seconds

data size: 50000

time: 86.9194 seconds

data size: 55000

time: 90.1077 seconds

data size: 60000

time: 125.0228 seconds

data size: 65000

time: 149.1858 seconds

data size: 70000

time: 177.4184 seconds

data size: 75000

time: 204.0712 seconds

data size: 80000

time: 220.502 seconds

data size: 85000

time: 251.7625 seconds

data size: 100000

time: 257.563 seconds

noisy data chart

The Stability

The algorithm is only single-parameter, even more it not sensitive to changes in that parameter, k. You may guess that from the following chart yourself. This is of greate benfit for you as a data exploration analyst. You can simply explore the dataset using an arbitrary k. Being Non-senstive to changes in k, make it robust and stable.

DenMune Stability chart

Reveal the propagation

one of the top performing feature in this algorithm is enabling you to watch how your clusters propagate to construct the final output clusters. just use the parameter ‘prop_step’ as in the following example:

dataset = "t7.10k" #
data_path = 'datasets/denmune/chameleon/'

# train file
data_file = data_path + dataset +'.csv'
X_train = pd.read_csv(data_file, sep=',', header=None)


from itertools import chain

# Denmune's Paramaters
knn = 39 # number of k-nearest neighbor, the only parameter required by the algorithm

# create list of differnt snapshots of the propagation
snapshots = chain(range(2,5), range(5,50,10), range(50, 100, 25), range(100,500,100), range(500,2000, 250), range(1000,5500, 500))

from IPython.display import clear_output
for snapshot in snapshots:
    print ("itration", snapshot )
    clear_output(wait=True)
    dm = DenMune(train_data=X_train, k_nearest=knn, rgn_tsne=False, prop_step=snapshot)
    labels, validity = dm.fit_predict(show_analyzer=False, show_noise=False)

How to Run and Test

Interact with the algorithm

chameleon datasets

This notebook allows you interact with the algorithm in many asspects:

  • you can choose which dataset to cluster (among 4 chameleon datasets)

  • you can decide which number of k-nearest neighbor to use

  • show noise on/off; thus you can invesitigate noise detected by the algorithm

  • show analyzer on/off

Repo2Docker Binder

Launch Examples in Repo2Docker Binder

Simply use our repo2docker offered by mybinder.org, which encapsulate the algorithm and all required data in one virtual machine instance. All jupter notebooks examples found in this repository will be also available to you in action to practice in this respo2docer. Thanks mybinder.org, you made it possible!

Launch notebook examples in Binder

Kaggle workspace

Launch each Example in Kaggle workspace

If you are a kaggler like me, then Kaggle, the best workspace where data scientist meet, should fit you to test the algorithm with great experince.

Dataset

Kaggle URL

When less means more - kaggle

When less means more - kaggle

Non-groundtruth datasets - kaggle

Non-groundtruth datasets

2D Shape datasets - kaggle

2D Shape datasets - kaggle

MNIST dataset kaggle

MNIST dataset - kaggle

Iris dataset kaggle

iris dataset - kaggle

Training MNIST to get 97%

Training MNIST to get 97%

Noise detection - kaggle

Noise detection - kaggle

Validation - kaggle

Validation - kaggle |

The beauty of propagation - kaggle

The beauty of propagation - kaggle

The beauty of propagation part2 - kaggle

The beauty of propagation part 2 - kaggle

Snapshots of propagation -kaggle

The beauty of propagation - kaggle

Scalability kaggle

Scalability - kaggle

Stability - kaggle

Stability - kaggle

k-nearest-evolution - kaggle

k-nearest-evolution - kaggle

Google Research, CoLab

Launch each Example in Google Research, CoLab

Need to test examples one by one, then here another option. Use colab offered by google research to test each example individually.

Here is a list of Google CoLab URL to use the algorithm interactively

Dataset

CoLab URL

How to use it - colab

How to use it - colab

Chameleon datasets - colab

Chameleon datasets - colab

2D Shape datasets - colab

2D Shape datasets - colab

MNIST dataset - colab

MNIST dataset - colab

iris dataset - colab

iris dataset - colab

Get 97% by training MNIST dataset - colab

Get 97% by training MNIST dataset - colab

Non-groundtruth datasets - colab

Non-groundtruth datasets - colab

Noise detection - colab

Noise detection - colab

validation - colab

Validation - colab

How it propagates - colab

How it propagates - colab

Snapshots of propagation - colab

snapshots of the propagation - colab

Scalability - colab

Scalability - colab

Stability vs number of nearest neighbors - colab

Stability vs number of nearest neighbors - colab

k-nearest-evolution - colab

k-nearest-evolution - colab

How to cite

If you have used this codebase in a scientific publication and wish to cite it, please use the Journal of Pattern Recognition article

Mohamed Abbas McInnes, Adel El-Zoghaby, Amin Ahoukry, *DenMune: Density peak based clustering using mutual nearest neighbors*
In: Journal of Pattern Recognition, Elsevier, volume 109, number 107589.
January 2021

@article{ABBAS2021107589,
title = {DenMune: Density peak based clustering using mutual nearest neighbors},
journal = {Pattern Recognition},
volume = {109},
pages = {107589},
year = {2021},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2020.107589},
url = {https://www.sciencedirect.com/science/article/pii/S0031320320303927},
author = {Mohamed Abbas and Adel El-Zoghabi and Amin Shoukry},
keywords = {Clustering, Mutual neighbors, Dimensionality reduction, Arbitrary shapes, Pattern recognition, Nearest neighbors, Density peak},
abstract = {Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm “DenMune” is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high dimensional datasets relative to several known state of the art clustering algorithms.}
}

Licensing

The DenMune algorithm is 3-clause BSD licensed. Enjoy.

BSD 3-Clause “New” or “Revised” License"

Task List

  • ☒ Update Github with the DenMune sourcode

  • ☒ create repo2docker repository

  • ☒ Create pip Package

  • ☒ create CoLab shared examples

  • ☒ create documentation

  • ☒ create Kaggle shared examples

  • ☐ create conda package