SS-DBSCAN: Semi-Supervised Density-Based Spatial Clustering of Applications With Noise for Meaningful Clustering in Diverse Density Data

SS-DBSCAN: Semi-Supervised Density-Based Spatial Clustering of Applications With Noise for Meaningful Clustering in Diverse Density Data

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised clustering algorithm designed to identify clusters of various shapes and sizes in noisy datasets by pinpointing core points. The primary challenges associated with the DBSCAN algorithm involve the recognition of...

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Bibliographic Details
Main Authors: Tiba Zaki Abdulhameed, Suhad A. Yousif, Venus W. Samawi, Hasnaa Imad Al-Shaikhli
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Clustering
DBSCAN
semi-supervised clustering
unsupervised classification
word classification
Online Access:https://ieeexplore.ieee.org/document/10670579/

Internet

https://ieeexplore.ieee.org/document/10670579/

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