Comparative Analysis of the Impact of Arrival Rates on the Performance of Distance-based Streaming Outlier Detection Algorithms
2021-05
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Comparative Analysis of the Impact of Arrival Rates on the Performance of Distance-based Streaming Outlier Detection Algorithms
Authors
Published Date
2021-05
Publisher
Type
Thesis or Dissertation
Abstract
Outlier detection in data streams comes with many challenges. Among these challenges is the variable arrival rate of streams. When data packets are sent across an unreliable network, the data sending process is interrupted due to temporary loss of signal and later all of the data is tried to send at once as signals resume, resulting in data point drop, leading to faulty outlier detection. However, which algorithm performs the best in such cases remained a question until now. This research studies the impact of the arrival rate, varying queue capacity sizes, and slide sizes on the performance of state-of-the-art outlier detection algorithms for data streams. Our experiments show that using a bounded queue for incoming data points and allowing data drop has an average detrimental impact on the F-1 score, which is 100% for NETS, 99.78% for Thresh-Leap, 99.69% for Micro-Cluster, 67.5% for Exact Storm, and 0.422% for DUE. The number of outliers lost is 0% for Thresh-Leap, -0.33% for NETS, 0% for Micro-Cluster, 39.2% for Exact Storm and 38.6% for DUE, observed on default parameters.
Description
University of Minnesota M.S. thesis. May 2021. Major: Computer Science. Advisor: Eleazar Leal. 1 computer file (PDF); vi, 57 pages.
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Durrani, Areeha. (2021). Comparative Analysis of the Impact of Arrival Rates on the Performance of Distance-based Streaming Outlier Detection Algorithms. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/223092.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.