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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 anddoi:10.1109/icdm.2012.127 dblp:conf/icdm/HuangQYY12 fatcat:rp5gib6dtbdcpchnxayuk6g5uy