Update 'README.md'

README.md

@@ -10,6 +10,8 @@ This Git repository contains the code that accompanies the research paper "t-viS
 
 **Note**: This software is based on the bhtsne library, its native executable and the python interface that is used to call the native executable. This library is the official implementation of t-SNE, made by its authors. Using the exact same input data, different systems will generate slightly different outputs in this library, and such differences will propagate to our software.
 
+**Note:** As any other software, the code is not bug free. There might be limitations in the views and functionalities of the tool that could be addressed in a future code update.
+
 # Data Sets #
 All data sets used in the paper are in the `data` folder, formatted as comma separated values (csv). 
 Most of them are available online from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/index.php): Iris, Breast Cancer Wisconsin (Original), Pima Indians Diabetes, and SPECTF. We also used a custom-made data set with Gaussian clusters. 
@@ -33,9 +35,8 @@ For the frontend:
 
 There is no need to install anything for the frontend, since all modules are in the repository.
 
-
 # Usage #
-Below is an example of how you can get t-viSNE running using Python for both frontend and backend. The frontend is written in Javascript/HTML, so it could be hosted in any other web server of your preference. The only hard requirement (currently) is that both frontend and backend must be running on the same machine. 
+Below is an example of how you can get t-viSNE running using Python for both frontend and backend. The frontend is written in JavaScript/HTML, so it could be hosted in any other web server of your preference. The only hard requirement (currently) is that both frontend and backend must be running on the same machine. 
 ```
 # first terminal: hosting the visualization side (client)
 # for Python3
@@ -72,4 +73,4 @@ The following instructions describe how to reach the results present in Figure 1
 **Outcome:** The above process describes how you will be able to reproduce precisely the results presented in Figures 1 and 7 of the paper. Thank you for your time!
 
 # Corresponding Author #
-For any questions with regard to the implementation or the paper, feel free to contact [Angelos Chatzimparmpas](mailto:angelos.chatzimparmpas@lnu.se).
+For any questions with regard to the implementation or the paper, feel free to contact [Angelos Chatzimparmpas](mailto:angelos.chatzimparmpas@lnu.se).