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DOI: 10.14569/IJACSA.2021.0121185
PDF

A Fast and Efficient Algorithm for Outlier Detection Over Data Streams

Author 1: Mosab Hassaan
Author 2: Hend Maher
Author 3: Karam Gouda

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 11, 2021.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Outlier detection over data streams is an important task in data mining. It has various applications such as fraud detection, public health, and computer network security. Many approaches have been proposed for outlier detection over data streams such as distance-,clustering-, density-, and learning-based approaches. In this paper, we are interested in the density-based outlier detection over data streams. Specifically, we propose an improvement of DILOF, a recent density-based algorithm. We observed that the main disadvantage of DILOF is that its summarization method has many drawbacks such as it takes a lot of time and the algorithm accuracy is significant degradation. Our new algorithm is called DILOFC that utilizing an efficient summarization method. Our performance study shows that DILOF􀀀 outperforms DILOF in terms of total response time and outlier detection accuracy.

Keywords: Data mining; outlier detection; data streams; density-based approach; clustering-based approach

Mosab Hassaan, Hend Maher and Karam Gouda, “A Fast and Efficient Algorithm for Outlier Detection Over Data Streams” International Journal of Advanced Computer Science and Applications(IJACSA), 12(11), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121185

@article{Hassaan2021,
title = {A Fast and Efficient Algorithm for Outlier Detection Over Data Streams},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121185},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121185},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {11},
author = {Mosab Hassaan and Hend Maher and Karam Gouda}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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