Compare commits

...

3 Commits

Author SHA1 Message Date
Angelos Chatzimparmpas 19f6f21e82 Update 'README.md' 2 years ago
Angelos Chatzimparmpas 66e4f7e056 Update 'README.md' 3 years ago
Angelos Chatzimparmpas fd3c3c2479 Update 'README.md' 4 years ago
  1. 9
      README.md

@ -1,7 +1,5 @@
# t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections #
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/56e643b253bf40afb9d788c0643c6940)](https://www.codacy.com/manual/Angelos-Chatzimparmpas/t-viSNE?utm_source=github.com&utm_medium=referral&utm_content=angeloschatzimparmpas/t-viSNE&utm_campaign=Badge_Grade)
This Git repository contains the code that accompanies the research paper "t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections". The details of the experiments and the research outcome are described in [the paper](https://doi.org/10.1109/TVCG.2020.2986996).
**Note:** t-viSNE is optimized to work better for standard resolutions (such as 1440p/QHD (Quad High Definition) and 1080p). Any other resolution might need manual adjustment of your browser's zoom level to work properly.
@ -10,6 +8,8 @@ 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 +33,8 @@ 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 +71,4 @@ 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).

Loading…
Cancel
Save