DBSCAN Clustering for Data Segmentation

https://colab.research.google.com/drive/1KAH6uNrgRoXlqRB10AeWeMTE8Bq2_Hi4?usp=sharing

Objective

To explore and apply the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to segment data into meaningful clusters, identify outliers, and analyze patterns in a given dataset.

Key Highlights

• Clustering Algorithm: DBSCAN, a density-based algorithm that groups closely packed data points while identifying noise as outliers.

• Dataset: Simulated multi-cluster data with noise for testing the clustering capabilities of DBSCAN.

• Tools Used:

  • Python: Programming language for data manipulation and visualization.

  • Scikit-learn: For implementing DBSCAN and preprocessing data with StandardScaler.

  • Matplotlib: For creating interactive visualizations.

Steps in the Project

1. Data Preparation:

   • Generated synthetic data with multiple clusters and noise using make_blobs.

   • Scaled the data using StandardScaler to standardize features for better clustering performance.

2. Modeling with DBSCAN:

   • Applied the DBSCAN algorithm with tuned hyperparameters:

     • eps: Maximum distance between points to consider them part of the same neighborhood.

     • min_samples: Minimum number of points required to form a dense region (a cluster).

   • Successfully identified core points, border points, and outliers.

3. Visualization of Results:

   • Created a scatter plot of clusters, where each cluster is represented by a unique color, and noise points are shown in black.

   • Demonstrated how DBSCAN effectively distinguishes dense regions and isolates outliers.

4. Results:

   • Identified 4 distinct clusters and outliers in the dataset.

   • Highlighted the algorithm’s robustness in handling datasets with varying densities and noise.

Visualization

The visualization above showcases the results of DBSCAN clustering:

• Clusters: Each cluster is assigned a unique color.

• Noise Points: Points classified as noise are marked in black, illustrating DBSCAN’s ability to handle outliers effectively.

Learning Outcomes

• Gained hands-on experience in implementing and fine-tuning the DBSCAN algorithm.

• Learned how to preprocess and scale data to improve clustering accuracy.

• Demonstrated the algorithm’s practical application in segmenting data with irregular cluster shapes and noise.

• Strengthened skills in creating clear and visually engaging representations of complex clustering results.