Wrapper (data mining)

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As a wrapper is called in computer science sub- field of information extraction, a group of special procedures for automatic extraction of ( semi-) structured data from a specific data source ( text). Here, the different wrappers to extract data records are needed depending on the type. In connection with Feature Subset Selection In addition, there are different approaches to the selection of an optimal set of feature subsets from the data sets.

General

• Background
• Historical Development
• Today's practical applications
• Legal aspects

LR wrapper

An LR wrapper consists of bounding pairs

Foreach

Limitations:

• Each must be a "real" suffix of the text before each instance of the target object. Real is, it must precede any instance and may occur anywhere else. Otherwise erroneous tuples are extracted.
• Each must be a prefix of the text following each instance of the target object. Otherwise, the extraction is aborted.

Source:

More wrapper

Source:

Wrappers and FSS

Some simple ways of selection consist of:

Limitations

• No permutations of possible attributes
• The boundary pairs are possibly not sufficient for the identification of texts

To solve these problems, different algorithms must be used for information extraction, such as a non - deterministic, adaptive Mealy machine (eg SoftMealy ) who does not have these limitations.