Optimizing DBSCAN Density-Based Clustering Algorithm For Enhanced Performance in Data Mining
Volume: 13 - Issue: 11 - Date: 01-11-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU427 | Page No.: 108-114
Author: Uvaish Akhter
Co- Author: Mr. Jeetendra Singh Yadav
Abstract:-Data mining techniques play a crucial role in extracting valuable insights from large datasets,
with clustering methods being among the most widely used. The Density-Based Spatial Clustering of
Applications with Noise (DBSCAN) algorithm is notable for its ability to identify clusters of varying shapes
while effectively handling noise. However, DBSCAN faces limitations with high-dimensional data and
varying density clusters, which restrict its performance in complex datasets. This thesis investigates methods
to enhance the performance of DBSCAN, focusing on optimizing parameters, improving computational
efficiency, and addressing density variations within clusters. We propose an advanced DBSCAN framework
that integrates adaptive parameter selection and novel density-based heuristics to improve accuracy and
scalability in high-dimensional data mining applications. Experimental results demonstrate that the
enhanced DBSCAN algorithm achieves superior clustering accuracy, reduced computational time, and
improved noise resilience compared to the traditional DBSCAN. These findings highlight the enhanced
DBSCAN's potential as a robust clustering solution for real-world data mining tasks, particularly in
scenarios involving large, complex datasets.
Key Words:-Data Mining, Clustering Algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), Performance Optimization, Density Variations, High-Dimensional Data, Noise Handling, Parameter Tuning, Adaptive Clustering
Area:-Engineering
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