GFF

This code is a new framework for feature analysis where a graph/tree layout is used to represent and explore patterns in features. The methodology is explained in the article "Graphs from Features: Tree-based Graph Layout for Feature Analysis" that is available at Algorithms Journal.

Authors

Rosane Minghim, Liz Huancapaza, Erasmo Artur, Guilherme P. Telles and Ivar V. Belizario

Prerequisites

From Linux or Windows, verify whether the following software are installed:

• MongoDB - 4.4+;
• Docker - 19.03.14+;
• GCC - C/C++;
• Python - Python 3.8+;
• Chrome - Chrome 85.0.4183+;

Running with docker (making a local image) 🆕

From Ubuntu, Windows or Mac run:

• docker build -t gffos .
• docker run -d -it -v "$(pwd)":/app/gff -p 8000:8000 gffos
• open the webbrowser with the url: http://localhost:8000/
• please login with user: localuser and password: gff

obs.: run "docker build -t gffos ." only once

obs.: run "docker stop id" to stop the gff application

obs.: run "docker start id" to start the gff application

obs.: id is dinamicaly asingned, use "docker ps" to see id

obs.: from Ubuntu use sudo for each command

Running directly

From Ubuntu 20.04:

• install MongoDB
• run: bash GFF.sh install
• run: bash GFF.sh
• please login with user: localuser and password: gff

obs.: don't forget to install MongoDB first.

Getting started

• Upload some CSV file data set and begin to explore:

Sample data sets

We have included two sample data sets that can help you to get started within the framework. The data sets are:

• MNIST: download

  • Content: Handwritten digits features
  • 784 features; 10,000 items
  • Target feature: label

• Corel Images: download

  • Content: Extracted image features
  • 150 features; 1000 items
  • Target feature: Category

Citation

@Article{minghim2020gff,
AUTHOR = {Minghim, Rosane and Huancapaza, Liz and Artur, Erasmo and Telles, Guilherme P. and
Belizario, Ivar V.},
TITLE = {Graphs from Features: Tree-Based Graph Layout for Feature Analysis},
JOURNAL = {Algorithms},
VOLUME = {13},
YEAR = {2020},
NUMBER = {11},
ARTICLE-NUMBER = {302},
URL = {https://www.mdpi.com/1999-4893/13/11/302},
ISSN = {1999-4893},
ABSTRACT = {Feature Analysis has become a very critical task in data analysis and
visualization. Graph structures are very flexible in terms of representation and may encode
important information on features but are challenging in regards to layout being adequate
for analysis tasks. In this study, we propose and develop similarity-based graph layouts
with the purpose of locating relevant patterns in sets of features, thus supporting feature
analysis and selection. We apply a tree layout in the first step of the strategy, to
accomplish node placement and overview based on feature similarity. By drawing the
remainder of the graph edges on demand, further grouping and relationships among features
are revealed. We evaluate those groups and relationships in terms of their effectiveness in
exploring feature sets for data analysis. Correlation of features with a target categorical
attribute and feature ranking are added to support the task. Multidimensional projections
are employed to plot the dataset based on selected attributes to reveal the effectiveness
of the feature set. Our results have shown that the tree-graph layout framework allows for
a number of observations that are very important in user-centric feature selection, and not
easy to observe by any other available tool. They provide a way of finding relevant and
irrelevant features, spurious sets of noisy features, groups of similar features, and
opposite features, all of which are essential tasks in different scenarios of data
analysis. Case studies in application areas centered on documents, images and sound data
demonstrate the ability of the framework to quickly reach a satisfactory compact
representation from a larger feature set.},
DOI = {10.3390/a13110302}
}