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Indexing
Note on Indexing Types
There is a spectrum of algorithms that produce an indexing of invariance in data.
The following basic classes exists:
1) Statistical
2) Rule based
3) Distributed
However, creating taxonomy for indexing algorithms is difficult.
In systems theory it would be claimed that the natural-type of problems to be solved have not found clear winners. On the other hand we can make the following distinction between types of problem.
Class 1: Many indexing problems are solved with some variation of statistical analysis leading to probabilities of relationships, followed by rule formation and hard coding of relationships.
Class 2: Other indexing problems are too non-stationary for statistical analysis. Distributed systems with recontextualization are generally applied. The distributed systems provide a transfer (learning) function and will work well if proper context is supplied by external source - sometimes but not always by a user.
Class 3: Some indexing problems are characterized by incomplete or uncertain information and data. Inference systems and knowledge acquisition methodology is used in these cases.