On the Subject of Identifying Anomalies in Heterogeneous Vector Based Data.

Cousins, Cyrus B.

Abstract:This paper is focused novel techniques for the unsupervised anomaly detection algorithm over datasets of real and finite discrete variables, centered around the existing FRaC algorithm. Novel variants of the FRaC algorithm are presented, alongside mathematical justification and empirical evidence to support their use. mFRaC, eFRaC and cFRaC, are introduced here. These techniques have a... read more

This object is in collection:
Undergraduate Honors Theses
Heterogeneous computing.
Anomaly detection (Computer security)
Tufts University. Department of Computer Science.
Permanent URL
ID: tufts:sd.0000231
To Cite: DCA Citation Guide
Usage: Detailed Rights