An online algorithm for detecting anomalies using fuzzy clustering

Darrin M. Hanna, Michael F. Lohrer, David E. Stern, Alexander Postlmayr, Adam Kollin, Shuo Wang, Gang-yu Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A fuzzy clustering algorithm with the ability to learn unsupervised can be used to detect objects of interest in semi-structured data. An online application of a fuzzy clustering algorithm with merging was implemented in both software and hardware to test anomaly detection in atomic force microscopy (AFM). The requisite components of the algorithm were all estimated, measured, and verified to meet real time constraints for incoming data. After clusters have been formed, representing the background of an image, any new cluster is an abnormality to the surface which is of interest to the user. This real-time detection of anomalies is important for identifying regions of interest for faster and higher resolution scanning. Results show this application is successfully capable of detecting anomalies in AFM topographic images. The approach taken in this paper is generic and can be applied to other applications with a continuous data stream.

Original languageEnglish (US)
Title of host publication2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018
EditorsDavid de la Fuente, Jose A. Olivas, Fernando G. Tinetti, Elena B. Kozerenko, Hamid R. Arabnia
PublisherCSREA Press
Pages433-439
Number of pages7
ISBN (Electronic)1601324804, 9781601324801
StatePublished - Jan 1 2018
Event2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Las Vegas, United States
Duration: Jul 30 2018Aug 2 2018

Publication series

Name2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018

Conference

Conference2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018
CountryUnited States
CityLas Vegas
Period7/30/188/2/18

Keywords

  • Anomaly detection
  • Atomic force microscopy
  • Fuzzy clustering

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'An online algorithm for detecting anomalies using fuzzy clustering'. Together they form a unique fingerprint.

  • Cite this

    Hanna, D. M., Lohrer, M. F., Stern, D. E., Postlmayr, A., Kollin, A., Wang, S., & Liu, G. (2018). An online algorithm for detecting anomalies using fuzzy clustering. In D. de la Fuente, J. A. Olivas, F. G. Tinetti, E. B. Kozerenko, & H. R. Arabnia (Eds.), 2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018 (pp. 433-439). (2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018). CSREA Press.