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Detection of Similarly-structured Anomalous sets of nodes in Graphs

Sharma, Nikita
http://orcid.org/0000-0002-6057-6920
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627665644265336

Abstract Details

2021, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Detecting anomalies in a given data set has been a vital task always with various applications in the areas of healthcare, banking, security and law enforcement. While there have been numerous methods and algorithms being developed in the past for anomaly detection, the technique of biclustering numerical data with the help of Triadic Concept Analysis (TCA) as an extension of FCA (Formal Concept Analysis) for ternary relations have started surfacing only recently. We have used this idea along with a very efficient algorithm called as 'TRIMAX Biclustering Algorithm' to find out anomalous biclusters in our data set for a given 'Theta' parameter. This Theta parameter is the condition under which a given node-attribute pair is identi ed as being similar. The technique of biclustering helps in overcoming the limitation of standard clustering techniques where distance function producing partitions of objects takes into consideration all attributes as this method may be ineffective or difficult to interpret. A Bicluster shows a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data set up which when combined with the statistical concept of Z-Score helps in finding anomalous biclusters in a given data set. This method is flexible and can be scaled to an n-dimensional numerical data set. We go a step further to verify whether the identi ed biclusters are persistent or not with the change in the 'Theta' parameter. Finally, we present three real-world applications of graph-based anomaly detection of a varying domain, size, shape and density.
Raj Bhatnagar, Ph.D. (Committee Chair)
Yizong Cheng, Ph.D. (Committee Member)
Nan Niu, Ph.D. (Committee Member)
92 p.
Computer Science
Anomaly Detection; Biclustering; TRIMAX Biclustering; Graph attributes

Recommended Citations

Citations

  • Sharma, N. (2021). Detection of Similarly-structured Anomalous sets of nodes in Graphs [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627665644265336

    APA Style (7th edition)

  • Sharma, Nikita. Detection of Similarly-structured Anomalous sets of nodes in Graphs. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627665644265336.

    MLA Style (8th edition)

  • Sharma, Nikita. "Detection of Similarly-structured Anomalous sets of nodes in Graphs." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627665644265336

    Chicago Manual of Style (17th edition)

ucin1627665644265336
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© 2021, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.