Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. License
5. Contact
6. Acknowledgments

About The Project

SimViz (Similarity VisualiZation) is a tool focused on the interactive visualization of similarity functions and how they are computed over different case bases.

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Built With

• 
• 
• 
• Webpack
• Handlebars

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

Install the prerrequisites with:

• npm

  npm install
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Installation

1. Clone the repo

   git clone https://github.com/gjimenezUCM/simviz.git
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2. Install NPM packages

   npm install
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3. The application is built using Webpack. To do it:

   npm run build
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4. During development, run a development server with:

   npm run start
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Usage

The main interface of SimViz is organized in three columns or panels:

– Similarity selection and description panel (left): Users employ this panel to select a case base to visualize and to choose a similarity function over this case base. – Similarity value distribution panel (middle): a heatmap and a histogram shows information about the distribution of the similarity values over the case base. Both are interactive and will help to explore the cases and similarity values. – Case comparator panel (right): a table that displays side by side two cases selected using the similarity value distribution panel. The global similarity value is shown on the header while the local similarity values and the attributes involved in the similarity calculation are displayed in rows, with a bar displaying the attribute weight on the first cell of each row.

Datasets/Case bases

Right now, with SimViz we can explore four different datasets:

• Blood Alcohol Domain
• Breast Cancer Wisconsin
• Travel Agent
• DMH Dataset

The first ones use some basic local similarity metrics for numbers and nominal attribute values. The DMH dataset contains 64 artwork descriptions from the Design Museum Helsinki, which imposed the definition of new similarity functions for color perception and emotions.

Similarity data

Similarity data is computed offline using a weighted average as global similarity function, and predefined local similarity functions over the attributes of the cases contained in a case base. The case base and the similarity data are enriched with information about attribute datatypes, local similarity functions, weights for global similarity functions and user explanations.

The data folder in this repository repository contains a notebook with some examples about how the current data was created to be visualized in the tool

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License

Distributed under the APACHE 2.0 License. See LICENSE-2.0.txt for more information.

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Contact

Your Name - @guille_fdi - gjimenez@ucm.es

Project Link: https://github.com/gjimenezUCM/simviz

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