MDL-ExtensionMDL-Extension

This is a project for the implementation of MDL (Minimum Description Length) based extensions. The MDL principle can be applied to get a shorter description of a dataset, using its regularities in reference to a suitable compression. So, the best description is seen as the one that compresses the dataset best. The MDL extension currently includes an operator for the implementation of KRIMP algorithm, which might be used to prune a set of frequent patterns.

This is a project for the implementation of MDL (Minimum Description Length) based extensions. The MDL principle can be applied to get a shorter description of a dataset, using its regularities in reference to a suitable compression. So, the best description is seen as the one that compresses the dataset best.

The MDL extension currently includes an operator for the implementation of KRIMP algorithm, which might be used to prune a set of frequent patterns. In particular, gaining a descriptive selection of frequent patterns is considerably due to the high redundancy and tremendous amount of patterns, that are generated by frequent pattern mining algorithms with loosened constraints (e.g. a lower support threshold). However, straightening these constraints results mostly in an output with few patterns that reveal nothing but common knowledge. KRIMP, or MDL based approaches in general, can be applied here to get a reasoning and understandable output of frequent patterns from a database. (For further informations visit patternsthatmatter.org.)

Additionally, algorithms based on the MDL principle are naturally guarded from overfitting because the length of the computed model is taken into account as much as the length of the encoded dataset when the overall size of the compressed dataset is computed.

Product Details

Version 5.0.0
File size 167 kB
Downloads 3604 (0 Today)3604 downloads
Vendor AI Group - TU Dortmund
Category Machine Learning
Released 9/3/13
Last Update 9/3/13 2:44 PM
(Changes)
License AGPL
Product web site http://www-ai.cs.uni-dortmund.de/auto?self=$dttc2lvzsw
Rating 0.0 stars(0)