Updated README

Author: alessi0x

README.md

@@ -1,64 +1,60 @@
 # pyclustertend
 
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-
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 [![DOI](https://zenodo.org/badge/187477036.svg)](https://zenodo.org/badge/latestdoi/187477036)
 
-pyclustertend is a python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.
-
-Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator :
+pyclustertend is a Python package specialized in cluster tendency. Cluster tendency consist to assess if clustering algorithms are relevant for a dataset.
 
-
-
-- [x] Hopkins Statistics
-- [x] VAT
-- [x] iVAT
-
-- [x] Metric based method (silhouette, calinksi, davies bouldin)
+Three methods for assessing cluster tendency are currently implemented and one additional method based on metrics obtained with a KMeans estimator:
+- Hopkins Statistics [^1]
+- VAT [^5]
+- iVAT [^6]
+- Metric based method (Silhouette [^2], Calinski-Harabasz [^3], Davies-Bouldin [^4])
 
 ## Installation
-
+To install the Python 3.14 compatible version of `pyclustertend`, you can use `pip` to install directly from the GitHub repository. Run the following command in your terminal:
+```shell
+pip install git+https://github.com/Alessi0X/pyclustertend-fork.git@upgrade-python-3.14
+```
+or, for the Python 3.13 compatible version:
 ```shell
-    pip install pyclustertend
+pip install git+https://github.com/Alessi0X/pyclustertend-fork.git@upgrade-python-3.13
 ```
+For info about the branches, please refer to the Acknowledgments section below.
 
 ## Usage
 
 ### Example Hopkins
 
 ```python
-    >>>from sklearn import datasets
-    >>>from pyclustertend import hopkins
-    >>>from sklearn.preprocessing import scale
-    >>>X = scale(datasets.load_iris().data)
-    >>>hopkins(X,150)
+    >>> from sklearn import datasets
+    >>> from pyclustertend import hopkins
+    >>> from sklearn.preprocessing import scale
+    >>> X = scale(datasets.load_iris().data)
+    >>> hopkins(X,150)
     0.18950453452838564
 ```
 
 ### Example VAT
 
 ```python
-    >>>from sklearn import datasets
-    >>>from pyclustertend import vat
-    >>>from sklearn.preprocessing import scale
-    >>>X = scale(datasets.load_iris().data)
-    >>>vat(X)
+    >>> from sklearn import datasets
+    >>> from pyclustertend import vat
+    >>> from sklearn.preprocessing import scale
+    >>> X = scale(datasets.load_iris().data)
+    >>> vat(X)
 ```
 
 <img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/vat.png" />
 
 ### Example iVat
 
 ```python
-    >>>from sklearn import datasets
-    >>>from pyclustertend import ivat
-    >>>from sklearn.preprocessing import scale
-    >>>X = scale(datasets.load_iris().data)
-    >>>ivat(X)
+    >>> from sklearn import datasets
+    >>> from pyclustertend import ivat
+    >>> from sklearn.preprocessing import scale
+    >>> X = scale(datasets.load_iris().data)
+    >>> ivat(X)
 ```
 
 <img height="350" src="https://raw.githubusercontent.com/lachhebo/pyclustertend/screenshots/ivat.png" />
@@ -67,3 +63,25 @@ Three methods for assessing cluster tendency are currently implemented and one a
 
 It's preferable to scale the data before using hopkins or vat algorithm as they use distance between observations. Moreover, vat and ivat algorithms
 do not really fit to massive databases. A first solution is to sample the data before using those algorithms. 
+
+## Acknowledgments
+
+This is a fork of the original [pyclustertend](https://github.com/lachhebo/pyclustertend) library by [Ismaïl Lachheb](https://github.com/lachhebo). The original library got somehow abandoned, and I decided to fork it and continue its development, especially in terms of compatibility with the latest versions of Python. This fork is structured as follows:
+- The original codebase is preserved in the `master` branch
+- The `upgrade-python-3.13` branch features updates to ensure compatibility with Python 3.13
+- The `upgrade-python-3.14` branch includes further updates for compatibility with Python 3.14
+
+Thank you to Ismaïl Lachheb for creating the original `pyclustertend` library!
+
+
+[^1]: Hopkins, Brian; Skellam, J.G. (1954). "A new method for determining the type of distribution of plant individuals". _Annals of Botany_. **18** (2). Annals Botany Co: 213–227. doi:10.1093/oxfordjournals.aob.a083391
+
+[^2]: Rousseeuw, Peter J. (1987). "Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis". _Computational and Applied Mathematics_. **20**: 53–65. doi:10.1016/0377-0427(87)90125-7.
+
+[^3]: Caliński, Tadeusz; Harabasz, Jerzy (1974). "A dendrite method for cluster analysis". _Communications in Statistics_. **3** (1): 1–27. doi:10.1080/03610927408827101
+
+[^4]: Davies, David L.; Bouldin, Donald W. (1979). "A Cluster Separation Measure". _IEEE Transactions on Pattern Analysis and Machine Intelligence_. PAMI-1 (2): 224–227. doi:10.1109/TPAMI.1979.4766909
+
+[^5]: Bezdek, James C.; Hathaway, Richard J. (2002). "VAT: A Tool for Visual Assessment of (Cluster) Tendency". _Proceedings of the 2002 International Joint Conference on Neural Networks_. IJCNN '02. IEEE Computer Society. pp. 2225–2230. doi:10.1109/IJCNN.2002.1007487.
+
+[^6]: Wang, L., Nguyen, U. T., Bezdek, J. C., Leckie, C. A., Ramamohanarao, K. (2010). "iVAT and aVAT: enhanced visual analysis for cluster tendency assessment". In _Advances in Knowledge Discovery and Data Mining. PAKDD 2010_ (pp. 16-27). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-13657-3_5.