Java Parallel Coordinates Visualization Tool, visualizing multidimensional/multivariate CSV data with Java Swing.
"Parallel Coordinates plots are a common method of visualizing high-dimensional datasets to analyze multivariate data having multiple variables, or attributes." Wikipedia
Make sure Java is installed on your system to compile and run this application.
To get started, open a command line interface (CLI) and navigate to your project directory. Run the following commands:
# Compile all Java files in the javaPC directory
javac javaPC/*.java
# Run the main Driver class
java javaPC.Driver
jar cvfm Parallel-Coordinates-Vis.jar manifest.txt javaPC/*.class
The Makefile in this project simplifies the process of compiling Java files and building the JAR file. Below are the provided recipes:
-
Compile Java Files and Build the JAR: This single command compiles all
.java
files within thejavaPC
directory and packages the compiled.class
files into a JAR file, including the specified manifest.make
-
Clean: Removes all compiled .class files and the generated JAR file to clean the project directory.
make clean
To plot a dataset make sure the class/id column is the last column.
- Start program with Java by running
JavaPC/Main.java
or by running the compiled JAR file. - Click 'Load CSV' to open the file picker and select a dataset to visualize.
- Click 'Render Plot', rerendering the plot will generate a new color scheme if preferred.
- Click 'Toggle Labels', to toggle on/off the visibility of class, attribute, and ranges.
- Click 'Histogram', to toggle on/off histogram correlated density sized vertices.
breast-cancer-wisconsin.csv
- UCI's Wisconsin breast cancer dataset with 30 features. Classes: Malignant, Benignbreast-cancer-wisconsin-9f.csv
- UCI's Wisconsin breast cancer dataset with 9 features. Classes: Malignant, Benigndiabetes.csv
- UCI's diabetes dataset. Classes: Negative, Positivefisher_iris_SVe.csv
- Subset of Fisher's Iris dataset for Versicolor and Setosa. Classes: Versicolor, Setosaheart.csv
- UCI's heart disease dataset. Classes: Absence, Presenceionosphere.csv
- Dataset of radar data. Classes: Good, Badiris.csv
- Fisher's Iris flower classification dataset. Classes: Virginica, Versicolor, Setosairis_S_vs_VW.csv
- Subset of Fisher's Iris dataset for Setosa vs. others. Classes: Setosa, Otheriris_setosa.csv
- Subset of Fisher's Iris dataset, only Setosa class. Classes: Setosairis_SVe_vs_Vi.csv
- Subset of Fisher's Iris dataset, comparing Setosa/Versicolor against Virginica. Classes: Setosa/Versicolor, Virginicairis_SVi_vs_Ve.csv
- Subset of Fisher's Iris dataset, comparing Setosa/Virginica against Versicolor. Classes: Setosa/Virginica, Versicoloriris_V_vs_V.csv
- Subset of Fisher's Iris dataset, comparing Virginica against others. Classes: Virginica, Othermnist_letters.csv
- MNIST's capital letter dimensions of handwriting dataset. Classes: Capital letters A - Zsin_cos.csv
- Dataset containing sin and cos values for various angles. Classes: Sin, Coswheat_seeds.csv
- UCI's wheat seeds dataset. Classes: 0, 1, 2wine.csv
- Dataset with chemical analysis of wines. Classes: Class_0, Class_1, Class_2