A Comparison between several Density-Based Anomaly Detection Approaches

Author(s)	Rajeev Tripathi, Alok Singh
Country	India
Abstract	The current world of internet, mobile devices, businesses, social media platforms, healthcare systems, and the Internet of Things all have a lot of data available online. The enormous volume of data, dimensionality, and dataset changes throughout time are these problems. Clustering algorithms are a useful tool for solving this type of problem. Consequently, the first step in resolving these issues is the application of clustering algorithms, which are necessary for data mining procedures to reveal the structure and hidden patterns in given datasets. Four clustering algorithms OPTICS (Ordering Points To Identify Clustering Structure), Hierarchical Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), AGGLOMERATIVE and HDBSCAN. The efficiency of each clustering approach is assessed using a range of external and internal parametric clustering assessment metrics.
Keywords	Deep learning, Clustering Algorithms, Density based Algorithms, Clustering in Presence of Noise.
Field	Computer Applications
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-07-14
Cite This	A Comparison between several Density-Based Anomaly Detection Approaches - Rajeev Tripathi, Alok Singh - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.24578
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.24578
Short DOI	https://doi.org/gt4gg8

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International Journal For Multidisciplinary Research