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Open AccessArticle

OFCOD: On the Fly Clustering Based Outlier Detection Framework

by 1,2,*,‡, 2,‡ and 2,‡
1
Computer Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
2
Computers and Control Engineering Department, Faculty of Engineering, Tanta University, Tanta 31733, Egypt
*
Author to whom correspondence should be addressed.
This paper is an extended version of A hybrid outlier detection algorithm based on partitioning clustering and density measures, Published in: 2015 Tenth International Conference on Computer Engineering & Systems (ICCES), 23–24 December 2015.
These authors contributed equally to this work.
Received: 31 October 2020 / Revised: 23 December 2020 / Accepted: 24 December 2020 / Published: 30 December 2020
(This article belongs to the Section Information Systems and Data Management)
In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics. View Full-Text
Keywords: clustering; outlier detection; outlierness factor; similarity measure clustering; outlier detection; outlierness factor; similarity measure
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MDPI and ACS Style

Elmogy, A.; Rizk, H.; Sarhan, A.M. OFCOD: On the Fly Clustering Based Outlier Detection Framework. Data 2021, 6, 1. https://0-doi-org.brum.beds.ac.uk/10.3390/data6010001

AMA Style

Elmogy A, Rizk H, Sarhan AM. OFCOD: On the Fly Clustering Based Outlier Detection Framework. Data. 2021; 6(1):1. https://0-doi-org.brum.beds.ac.uk/10.3390/data6010001

Chicago/Turabian Style

Elmogy, Ahmed; Rizk, Hamada; Sarhan, Amany M. 2021. "OFCOD: On the Fly Clustering Based Outlier Detection Framework" Data 6, no. 1: 1. https://0-doi-org.brum.beds.ac.uk/10.3390/data6010001

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