Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/5194
Title: Outlier Detection Using Density-Based Clustering: A Comparative Study of LOF and ARDOD Algorithms
Authors: Shah, Chandra Bikram
Kiran, Putcha Sai
Bhattacharjee, Panthadeep
Kumar, Arun
Keywords: LOF
Reachability distance
density
anomaly detection
outlier identifications
ARDOD
Issue Date: May-2025
Citation: 3rd International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), KIIT Deemed to be University, Bhubaneswar, 16-18 May 2025
Abstract: Outlier detection (OD) plays an important role in areas such as fraud detection, network security, and so on. In addition, traditional OD methods were limited to detecting a single type of anomaly: local, global, or clustered anomalies. For denser regions, extraction of outliers may turn out to be a challenging task. In order to address these issues, the paradigm of density-based outlier detection (DBOD) came into existence. DBOD mainly leverages the ideas of local outlier factor (LOF), the reachability distance, and density. The LOFbased OD approach is examined in detail to provide a clearer understanding. It is then compared with another widely used OD method: Adaptive Radius Density-Based Outlier Detection (ARDOD).
Description: Copyright belongs to the proceeding publisher
URI: http://hdl.handle.net/2080/5194
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2025_ASSIC25_PBhattacharjee_Outlier.pdf2.98 MBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.