Outlier detection in two review articles (Part 1) by Sandro Saitta.
Sandro writes:
The first one, Outlier Detection: A Survey, is written by Chandola, Banerjee and Kumar. They define outlier detection as the problem of “[…] finding patterns in data that do not conform to expected normal behavior“. After an introduction to what outliers are, authors present current challenges in this field. In my experience, non-availability of labeled data is a major one.
…
One of their main conclusions is that “[…] outlier detection is not a well-formulated problem“. It is your job, as a data miner, to formulate it correctly.
The final quote seems particularly well suited to subject identity issues. While any one subject identity may be well defined, the question is how to find and manage other subject identifications that may not be well defined.
As Sandro points out, it has nineteen (19) pages of references. However, only nine of those are as recent at 2007. All the rest are older references. I am sure it remains an excellent reference source but suspect more recent review articles on outlier detection exist.
Suggestions?