Hello,
This is my first post to the list, I have been recently in touch with
Alexandre Gramfort, and I would be very interested in exploring some
outlier/anomaly detection algorithms, before eventually put it in a
compatible scikit learn API (with a view to eventually merge it).
I'm not particularly aware of the state-of-the-art in the efficience of
such algorithms, I have just read some surveys and other litterature on it,
and my conclusion is that exploring the following classical methods would
be productive :
- density-based algorithms : LOF (Local Outlier Factor) and its variations
(other algorithms using relative density/k-NN) such as COF
(Connectivity-based Outlier Factor), ODIN (Outlier Detection using Indegree
Number), LOCI (Local Correlation Integral).
LOF : http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf
COF : http://www.cse.cuhk.edu.hk/~adafu/Pub/pakdd02.pdf
ODIN : ftp://193.167.42.127/pub/franti/papers/Hautamaki/P2.pdf
LOCI : http://www.dtic.mil/dtic/tr/fulltext/u2/a461085.pdf
- high-dimensional approach : « Aggarwal and Yu algorithm »
http://www.researchgate.net/publication/2401320_Outlier_Detection_for_High_Dimensional_Data/file/e0b49525c3e5f60b5e.pdf
- iForest (Isolation Forest), which seems very interesting because it does
not rely on any distance or density measure.
http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf?q=isolation
So please let me know if some of these algorithms (or others) may generate
a particular interest.
Anyway I'd be very glad to get any feedback on it.
Cheers,
Nicolas
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