A New Anomaly Detection Algorithm Based on Quantum Mechanics

Hao Huang, Hong Qin, Shinjae Yoo, Dantong Yu
2012 2012 IEEE 12th International Conference on Data Mining  
The primary originality of this paper lies at the fact that we have made the first attempt to apply quantum mechanics theory to anomaly (outlier) detection in highdimensional datasets for data mining. We propose Fermi Density Descriptor (FDD) which represents the probability of measuring a fermion at a specific location for anomaly detection. We also quantify and examine different Laplacian normalization effects and choose the best one for anomaly detection. Both theoretical proof and
more » ... proof and quantitative experiments demonstrate that our proposed FDD is substantially more discriminative and robust than the commonly-used algorithms.
doi:10.1109/icdm.2012.127 dblp:conf/icdm/HuangQYY12 fatcat:rp5gib6dtbdcpchnxayuk6g5uy