Abstract
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 quantitative experiments demonstrate that our proposed FDD is substantially more discriminative and robust than the commonly-used algorithms.
Original language | English (US) |
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Title of host publication | Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012 |
Pages | 900-905 |
Number of pages | 6 |
DOIs | |
State | Published - Dec 1 2012 |
Externally published | Yes |
Event | 12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium Duration: Dec 10 2012 → Dec 13 2012 |
Other
Other | 12th IEEE International Conference on Data Mining, ICDM 2012 |
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Country/Territory | Belgium |
City | Brussels |
Period | 12/10/12 → 12/13/12 |
All Science Journal Classification (ASJC) codes
- Engineering(all)