Merge branch 'master' of https://github.com/FrancoBobadilla/NeuralMap

README.md

@@ -1,7 +1,9 @@
 # NeuralMap
 
-NeuralMap is a **data analysis** tool to generate a **discrete**, **low-dimensional** representation of the input data 
-space using the **[Self Organizing Maps algorithm](https://en.wikipedia.org/wiki/Self-organizing_map)**. 
+NeuralMap is  new Python implementation of the well-known self organized artificial neural network, **[Self-Organizing Map (SOM)](https://ieeexplore.ieee.org/document/58325)**, with the **[Relative Position (RP) Visualization method](https://www.sciencedirect.com/science/article/abs/pii/S0010482507000844?via%3Dihub)**. The RP is a node-adaptive attribute that moves in a two dimensional space mimicking, at training stage, the movements of the SOM's codebook vectors in the input space. 
+
+In this way **NeuralMap** results in a powerfull **data analysis** tool that generates both a **low-dimensional** representation of the input data as tSNE or UMAP tools and placing codebook vectors in the input space providing a smoother version of the input space. In addition, it maps data similarity into both codebook vectors and RP neighborness, thus allowing clear identification of similarity in the two dimensional space, with the added advantage of precessing new inputs without retraining needs.
+
 
 This tool supports a **wide range of configurations**:
  - Custom **distance metric**
@@ -14,14 +16,14 @@ After training a **NeuralMap instance**, you will be able to get **useful inform
 observations to the SOM, watching the **features distribution** over the map, analysing a dataset to get the
 **quantization error**, **activation frequency** and **mean distance** for each node, and **evaluating** the SOM.
 
-Since the use of SOM for **clustering** is very spread, this tool employs some common clustering algorithms:
+Since the use of SOM for **clustering** is very spread, NeuralMap also includes several highly customizable **visualization methods**, some of them based on the 
+**[Relative Positions technique](https://www.researchgate.net/publication/6292810_Improving_cluster_visualization_in_self-organizing_maps_Application_in_gene_expression_data_analysis)** to improve the interpretability of results and clusters recognition.
+
+For instance, we have implemented some common clustering algorithms to search clusters into the SOM configuration, speeding up the search of clusters in the smoothed space representation achieved by the SOM codebook vectors or RP rather than in the, possible, high dimensional imput space:
  - [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/index.html)
  - [K-means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)
  - [K-medoids](https://scikit-learn-extra.readthedocs.io/en/latest/generated/sklearn_extra.cluster.KMedoids.html)
 
-And also includes several highly customizable **visualization methods**, some of them based on the 
-**[Relative Positions technique](https://www.researchgate.net/publication/6292810_Improving_cluster_visualization_in_self-organizing_maps_Application_in_gene_expression_data_analysis)** 
-to improve the interpretability of results and clusters recognition.
 
 ## Installation
 
@@ -61,7 +63,7 @@ nm = NeuralMap(5, 'euclidean', columns=10, rows=10)
 nm.train(data, n_epochs=20)
 ```
 
-Now we can obtain a **discrete representation**:
+Now we can obtain a **discrete representation** using the HB-SCAN over the codebook vectors and represented in the two dimensional space mapped trhough the Relative Positions:
 ```python
 nm.plot_analysis(data)
 ```
@@ -83,6 +85,12 @@ For more details, see the **[NeuralMap documentation]()**.
 
 If you have a **question**, please open an **issue**.
 
+## Authors
+
+* **Elmer Andrés Fernández** - *Original Idea* - [Profile](https://www.researchgate.net/profile/Elmer_Fernandez) - [CIDIE]- [CONICET](http://www.conicet.gov.ar) - [UCC](http://www.ucc.edu.ar)
+* **Franco Bobadilla** - *Developer* - Universidad Católica de Córdoba
+* **Pablo Pastore** - *Advice* - Universidad Católica de Córdoba
+
 ## Contributing
 
 To **contribute**, do the following: