A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is
Anomaly detection algorithms face several challenges, including processing speed and dealing with noise in data. In this thesis, a two-layer clusterbased anomaly detection structure is presented which is fast, noise-resilient and incremental. In this structure, each normal pattern is considered as a cluster, and each cluster is represented using a Gaussian Mixture Model (GMM). Then, new instances are presented to the GMM to be labeled as normal or abnormal. The proposed structure comprisesdoi:10.1016/j.ins.2017.11.023 fatcat:fisgkfropjh7fj3tpxoxdqszxa