OFCOD: On the Fly Clustering Based Outlier Detection Framework

Ahmed Elmogy, Hamada Rizk and Amany M. Sarhan
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Ahmed Elmogy: Computer Engineering Department, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia
Hamada Rizk: Computers and Control Engineering Department, Faculty of Engineering, Tanta University, Tanta 31733, Egypt
Amany M. Sarhan: Computers and Control Engineering Department, Faculty of Engineering, Tanta University, Tanta 31733, Egypt

Data, 2020, vol. 6, issue 1, 1-20

Abstract: 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.

Keywords: clustering; outlier detection; outlierness factor; similarity measure (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
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