druhg 0.10

Universal clustering based on dialectical materialism

DRUHG

DRUHG - Dialectical Ranking Universal Hierarchical Grouping (друг).

Performs clustering based on subjective rankings of each datapoint and builds minimum spanning tree.

Does not require parameters.

To compensate the inevitability of the result, user can set the limits on the size of the cluster with limit1 and limit2.

To get genuine result and genuine outliers set limit1 to 1.

Parameter fix_outliers allows to label outliers to their closest clusters via mstree edge.

Basic Concept

There are some optional tuning parameters but actual algorithm requires none and is universal.

It works by applying the universal society rule: treat others how you want to be treated.

The core of the algorithm is to rank subject’s closest subjective similarities and unite subjects into commonalities accordingly.

Parameter max_ranking controls precision vs productivity balance, after some value the precision and the result would not change.

Let’s say you have a list of friends and your number one friend is John, but you are number 5 on his friend list, then you would treat him as your number 5 friend.

todo: insert picture

Process of merging subjects and commonalities goes until the whole tree is a commonality.

Commonalities that were merged against sizeable commonalities become clusters.

Subjects in order to be merged have to be mutually close to each other and have to have the closest relationship among everyone else.

They have to reflect from all others and each other, and then because of that become one.

The reflections of the subjects are it’s key, only by reflecting in the other, the subject unveils it’s potential energy.

Cluster is the mutually-close reflections.

Read more in the paper(be aware, it is a translation) https://github.com/artamono/druhg/papers

How to use DRUHG

import sklearn.datasets as datasets
import druhg

iris = datasets.load_iris()
XX = iris['data']

clusterer = druhg.DRUHG(max_ranking=50)
labels = clusterer.fit(XX).labels_

It will build the tree and label the points. Now you can manipulate clusters by relabeling.

labels = dr.relabel(limit1=0, limit2=len(XX)/2, fix_outliers=1)
ari = adjusted_rand_score(iris['target'], labels)
print ('iris ari', ari)

Performance

It can be slow on a highly structural data.

There is a parameters max_ranking that can be used decreased for a better performance.

Installing

PyPI install, presuming you have an up to date pip:

pip install druhg

Running the Tests

The package tests can be run after installation using the command:

nosetests -s druhg

or, if nose is installed but nosetests is not in your PATH variable:

python -m nose -s druhg

The tests may fail :-D

Python Version

The druhg library supports both Python 2 and Python 3.

Contributing

We welcome contributions in any form! Assistance with documentation, particularly expanding tutorials, is always welcome. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.

Licensing

The druhg package is 3-clause BSD licensed.