Physics Maths Engineering
Feature weighting in DBSCAN using reverse nearest neighbours
Stiphen Chowdhury,
Na Helian,
Renato Cordeiro de Amorim
Renato Cordeiro de Amorim
School of Computer Science and Electronic Engineering
Peer Reviewed
Abstract
DBSCAN is arguably the most popular density-based clustering algorithm, and it is capable of recovering non-spherical clusters. One of its main weaknesses is that it treats all features equally. In this paper, we propose a density-based clustering algorithm capable of calculating feature weights representing the degree of relevance of each feature, which takes the density structure of the data into account. First, we improve DBSCAN and introduce a new algorithm called DBSCANR. DBSCANR reduces the number of parameters of DBSCAN to one. Then, a new step is introduced to the clustering process of DBSCANR to iteratively update feature weights based on the current partition of data. The feature weights produced by the weighted version of the new clustering algorithm, W-DBSCANR, measure the relevance of variables in a clustering and can be used in feature selection in data mining applications where large and complex real-world data are often involved. Experimental results on both artificial and real-world data have shown that the new algorithms outperformed various DBSCAN type algorithms in recovering clusters in data.
Key Questions
What is DBSCAN, and why is it popular?
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a widely used clustering algorithm that groups data points based on density. It is popular because it can find non-spherical clusters and identify outliers, making it versatile for many applications.
What is the main weakness of DBSCAN?
DBSCAN treats all features equally, which can be a problem when some features are more relevant than others for clustering. This can lead to suboptimal results, especially with complex or high-dimensional data.
What is DBSCANR, and how does it improve DBSCAN?
DBSCANR is an improved version of DBSCAN that reduces the number of parameters from two to one, simplifying its use. It maintains DBSCAN's ability to find non-spherical clusters while making the algorithm more user-friendly.
What is W-DBSCANR, and how does it work?
W-DBSCANR is a weighted version of DBSCANR that calculates feature weights during clustering. These weights represent the relevance of each feature, allowing the algorithm to focus on the most important features and improve clustering accuracy.
How are feature weights calculated in W-DBSCANR?
Feature weights are iteratively updated during the clustering process based on the current partition of the data. This ensures that the algorithm adapts to the density structure of the data and assigns higher weights to more relevant features.
What are the advantages of W-DBSCANR?
W-DBSCANR:
- Improves clustering accuracy by focusing on relevant features.
- Reduces the number of parameters, making it easier to use.
- Outperforms traditional DBSCAN and other variants in recovering clusters.
- Can be used for feature selection in data mining applications.
How was W-DBSCANR tested?
The algorithm was tested on both artificial and real-world datasets. Results showed that W-DBSCANR outperformed various DBSCAN-type algorithms in accurately recovering clusters, especially in complex and high-dimensional data.
What are the practical applications of W-DBSCANR?
W-DBSCANR is useful in:
- Data mining for identifying patterns in large, complex datasets.
- Feature selection to improve the performance of machine learning models.
- Anomaly detection in fields like fraud detection or network security.
How does W-DBSCANR compare to other clustering algorithms?
W-DBSCANR outperforms traditional DBSCAN and other variants by incorporating feature weights, which improve clustering accuracy. It is particularly effective for datasets with irrelevant or noisy features.
What are the future directions for this research?
Future research could explore:
- Extending W-DBSCANR to handle streaming or real-time data.
- Integrating it with deep learning for hybrid clustering models.
- Applying it to specific domains like bioinformatics or social network analysis.
