MetaCluster is the largest open-source nature-inspired optimization (Metaheuristic Algorithms) library for clustering problem in Python

• Free software: GNU General Public License (GPL) V3 license
• Provided 3 classes: MetaCluster, MhaKCentersClustering, and MhaKMeansTuner
• Total nature-inspired metaheuristic optimizers (Metaheuristic Algorithms): > 200 optimizers
• Total objective functions (as fitness): > 40 objectives
• Total supported datasets: 48 datasets from Scikit learn, UCI, ELKI, KEEL...
• Total performance metrics: > 40 metrics
• Total different way of detecting the K value: >= 10 methods
• Documentation: https://metacluster.readthedocs.io/en/latest/
• Python versions: >= 3.7.x
• Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics, plotly, kaleido

Citation Request

Please include these citations if you plan to use this library:

```code @article{VanThieu2023, author = {Van Thieu, Nguyen and Oliva, Diego and Pérez-Cisneros, Marco}, title = {MetaCluster: An open-source Python library for metaheuristic-based clustering problems}, journal = {SoftwareX}, year = {2023}, pages = {101597}, volume = {24}, DOI = {10.1016/j.softx.2023.101597}, }

@article{van2023mealpy, title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python}, author={Van Thieu, Nguyen and Mirjalili, Seyedali}, journal={Journal of Systems Architecture}, year={2023}, publisher={Elsevier}, doi={10.1016/j.sysarc.2023.102871} } ```

Installation

• Install the current PyPI release: bash $ pip install metacluster

After installation, check the version: ```bash $ python

import metacluster metacluster.version ```

Examples

We implement a dedicated Github repository for examples at MetaCluster_examples

Let's go through some basic examples from here:

1. First, load dataset. You can use the available datasets from MetaCluster:

```python

Load available dataset from MetaCluster

from metacluster import get_dataset

Try unknown data

get_dataset("unknown")

Enter: 1 -> This wil list all of avaialble dataset

data = get_dataset("Arrhythmia") ```

• Or you can load your own dataset

```python import pandas as pd from metacluster import Data

load X and y

NOTE MetaCluster accepts numpy arrays only, hence use the .values attribute

dataset = pd.readcsv('examples/dataset.csv', indexcol=0).values X, y = dataset[:, 0:-1], dataset[:, -1] data = Data(X, y, name="my-dataset") ```

2. Next, scale your features

You should confirm that your dataset is scaled and normalized

```python

MinMaxScaler

data.X, scaler = data.scale(data.X, method="MinMaxScaler", feature_range=(0, 1))

StandardScaler

data.X, scaler = data.scale(data.X, method="StandardScaler")

MaxAbsScaler

data.X, scaler = data.scale(data.X, method="MaxAbsScaler")

RobustScaler

data.X, scaler = data.scale(data.X, method="RobustScaler")

Normalizer

data.X, scaler = data.scale(data.X, method="Normalizer", norm="l2") # "l1" or "l2" or "max" ```

3. Next, select Metaheuristic Algorithm, Its parameters, list of objectives, and list of performance metrics

python list_optimizer = ["BaseFBIO", "OriginalGWO", "OriginalSMA"] list_paras = [ {"name": "FBIO", "epoch": 10, "pop_size": 30}, {"name": "GWO", "epoch": 10, "pop_size": 30}, {"name": "SMA", "epoch": 10, "pop_size": 30} ] list_obj = ["SI", "RSI"] list_metric = ["BHI", "DBI", "DI", "CHI", "SSEI", "NMIS", "HS", "CS", "VMS", "HGS"]

You can check all supported metaheuristic algorithms from: https://github.com/thieu1995/mealpy. All supported clustering objectives and metrics from: https://github.com/thieu1995/permetrics.

If you don't want to read the documents, you can print out all supported information by:

```python from metacluster import MetaCluster

Get all supported methods and print them out

MetaCluster.get_support(name="all") ```

4. Next, create an instance of MetaCluster class and run it.

```python model = MetaCluster(listoptimizer=listoptimizer, listparas=listparas, listobj=listobj, n_trials=3, seed=10)

model.execute(data=data, clusterfinder="elbow", listmetric=listmetric, savepath="history", verbose=False)

model.saveboxplots() model.saveconvergences() ```

As you can see, you can define different datasets and using the same model to run it. Remember to set the name to your dataset, because the folder that hold your results is the name of your dataset. More examples can be found here

Support

Official links (questions, problems)

• Official source code repo: https://github.com/thieu1995/metacluster
• Official document: https://metacluster.readthedocs.io/
• Download releases: https://pypi.org/project/metacluster/
• Issue tracker: https://github.com/thieu1995/metacluster/issues
• Notable changes log: https://github.com/thieu1995/metacluster/blob/master/ChangeLog.md

• Official chat group: https://t.me/+fRVCJGuGJg1mNDg1

• This project also related to our another projects which are optimization and machine learning. Check it here:

  • https://github.com/thieu1995/metaheuristics
  • https://github.com/thieu1995/mealpy
  • https://github.com/thieu1995/mafese
  • https://github.com/thieu1995/pfevaluator
  • https://github.com/thieu1995/opfunu
  • https://github.com/thieu1995/enoppy
  • https://github.com/thieu1995/permetrics
  • https://github.com/thieu1995/IntelELM
  • https://github.com/thieu1995/MetaPerceptron
  • https://github.com/thieu1995/GrafoRVFL
  • https://github.com/aiir-team

Supported links

code 1. https://jtemporal.com/kmeans-and-elbow-method/ 2. https://medium.com/@masarudheena/4-best-ways-to-find-optimal-number-of-clusters-for-clustering-with-python-code-706199fa957c 3. https://github.com/minddrummer/gap/blob/master/gap/gap.py 4. https://www.tandfonline.com/doi/pdf/10.1080/03610927408827101 5. https://doi.org/10.1016/j.engappai.2018.03.013 6. https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Clustering_metrics.ipynb 7. https://elki-project.github.io/ 8. https://sci2s.ugr.es/keel/index.php 9. https://archive.ics.uci.edu/datasets 10. https://python-charts.com/distribution/box-plot-plotly/ 11. https://plotly.com/python/box-plots/?_ga=2.50659434.2126348639.1688086416-114197406.1688086416#box-plot-styling-mean--standard-deviation