Table of Contents
This project aims to perform tree classification using the DBSCAN algorithm. Instead of using traditional coordinates, distances between points are employed for the classification.
If you would like to find out more about the project, the ideas for improvement, the difficulties encountered and the changes to be made, please read the "phyDBSCAN_Project_Report.pdf" in attachment.
Insert your dataset matrix in the "resources/input_data.txt" file, then use one of the two compilation methods.
Use the provided Makefile to install the project:
make
To run the project, execute:
./phyDBSCAN input.txt output.csv
To clean the project, execute:
make clean
Alternatively, if you are using Clion IDE, you can use CMake for building the project. Here are the steps:
- Run Clion IDE & Open the project
- Go to Run -> Edit Configurations
- Click on the "+" button and select "CMake"
- In the "Name" field, enter "phyDBSCAN" and fill information like in the following image:
- Click on "Apply" and "OK" and run the project
To test, we took a matrix from the "resources/input_simulation_dataset.txt" file
Input Data Set used in this example (distance matrix) we put in the file "resources/input_data.txt":
0 0.4 0.4 0.4 0.4 1 1 1 1 1 0.8 1 1 1 1 0.8 0.8 0.6 0.8 0.8
0.4 0 0.4 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1 0.8 1 1 0.8 0.8 0.8 0.8 0.8 0.8
0.4 0.4 0 0.8 0.8 1 1 1 1 1 1 1 0.8 0.8 1 0.8 0.8 0.8 0.8 0.8
0.4 0.8 0.8 0 0.6 1 1 1 1 1 0.8 1 1 1 1 0.6 0.6 0.4 0.6 0.6
0.4 0.8 0.8 0.6 0 1 1 1 1 1 0.6 0.8 0.8 0.8 0.8 1 1 0.8 1 1
1 0.8 1 1 1 0 0.4 0.4 0.4 0.4 1 0.8 1 1 0.8 1 1 0.8 1 1
1 0.8 1 1 1 0.4 0 0.6 0.4 0.6 1 0.6 1 1 0.6 1 1 0.8 1 1
1 0.8 1 1 1 0.4 0.6 0 0.6 0.6 1 0.8 1 1 0.8 1 1 0.8 1 1
1 0.8 1 1 1 0.4 0.4 0.6 0 0.6 1 0.8 1 1 0.8 1 1 0.8 1 1
1 0.8 1 1 1 0.4 0.6 0.6 0.6 0 1 0.8 1 1 0.8 1 1 0.8 1 1
0.8 1 1 0.8 0.6 1 1 1 1 1 0 0.4 0.4 0.4 0.4 1 1 0.8 1 1
1 0.8 1 1 0.8 0.8 0.6 0.8 0.8 0.8 0.4 0 0.4 0.4 0 1 1 1 1 1
1 1 0.8 1 0.8 1 1 1 1 1 0.4 0.4 0 0 0.4 1 1 1 1 1
1 1 0.8 1 0.8 1 1 1 1 1 0.4 0.4 0 0 0.4 1 1 1 1 1
1 0.8 1 1 0.8 0.8 0.6 0.8 0.8 0.8 0.4 0 0.4 0.4 0 1 1 1 1 1
0.8 0.8 0.8 0.6 1 1 1 1 1 1 1 1 1 1 1 0 0.4 0.4 0.4 0.4
0.8 0.8 0.8 0.6 1 1 1 1 1 1 1 1 1 1 1 0.4 0 0.4 0.6 0.6
0.6 0.8 0.8 0.4 0.8 0.8 0.8 0.8 0.8 0.8 0.8 1 1 1 1 0.4 0.4 0 0.6 0.6
0.8 0.8 0.8 0.6 1 1 1 1 1 1 1 1 1 1 1 0.4 0.6 0.6 0 0.6
0.8 0.8 0.8 0.6 1 1 1 1 1 1 1 1 1 1 1 0.4 0.6 0.6 0.6 0
In the "input_simulated_data.txt" file, the first line of this dataset is the following: 20 8 4 0 50
The first number (20) is the number of points in the dataset, the third number (4) is the number of clusters expected, it is used to calculate the ARI (Adjusted Rand Index).
The output of the program will be stored in the output.csv file as follows :
DBSCAN;0.490000;3;20;8;4;50;1.000000;(1<>1<>1<>1<>1<>2<>2<>2<>2<>2<>3<>3<>3<>3<>3<>4<>4<>4<>4<>4);462
DBSCAN : method used for the clustering
0.490000 : value of epsilon
3 : number of minimum points
20 : number of trees in the matrix
8 : number of leaves in each trees
4 : number of cluster we expect to find
50 : noise (differences between the trees within a cluster)
1.00000 : ARI
(<><><>) : partition
462 : time it took the program to calculate the clusters and ARI for the matrix
Please email us at : [email protected] or [email protected] for any question or feedback.