littlefish-coder / clustering

NCKU 112 Spring - 資料探勘與社群網路分析 Assignment 3

Clustering

To run the program, see the Usage section at the end of the document.

Introduction

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that groups together points that are closely packed together. It is also able to identify points that are outliers. The algorithm works by grouping together points that are within a certain distance of each other. The algorithm has two parameters: epsilon and minPts. Epsilon is the maximum distance between two points for them to be considered as in the same cluster. minPts is the minimum number of points required to form a cluster.

Results

1. Clustering_testdata/Clustering_test1

   • eps=0.25
   • min_samples=20
   • clusters: 4 (without noise)

2. Clustering_testdata/Clustering_test2

   • eps=0.20
   • min_samples=20
   • clusters: 4 (without noise)

3. Clustering_testdata/Clustering_test3

   • eps=0.23
   • min_samples=20
   • clusters: 5 (without noise) This dataset is a bit more complex than the previous two datasets. I've tried to find the optimal values for epsilon and min_samples. This is the best result I could get.

4. Clustering_testdata/Clustering_test4

   • eps=0.26
   • min_samples=25
   • clusters: 2 (without noise)
   • 

5. Clustering_testdata/Clustering_test5

   • eps=0.265
   • min_samples=20
   • clusters: 2 (without noise)

Final Output Format

PNG file: {input_file}_output.png

The output file is a PNG file that shows the clusters. The clusters are shown in different colors.

text file: {input_file}_output.txt

The text file contains the following information:

eps: {epsilon}, min_samples: {min_samples}
X Y ClusterID

Usage

clone the repository and run the following command in the terminal:

git clone https://github.com/LittleFish-Coder/clustering.git

cd clustering

python nm6121030.py

add arguments to the command line to change the default values of the program.

• --input to change the input file (default: Clustering_testdata/Clustering_test1)
• --eps to change the epsilon value (default: 0.20)
• --min_samples to change the min_samples value (default: 20)

Example

python nm6121030.py --input Clustering_testdata/Clustering_test2 --eps 0.25 --min_samples 20