JIM - Jaccard Item Set Mining

Download

Description

A program to find Jaccard item sets with an extension of the Eclat algorithm. In analogy to frequent item set mining, where one tries to find item sets the support of which exceeds a user-specified threshold (minimum support) in a database of transactions, a Jaccard item set is an item set for which the (generalized) Jaccard index of its item covers exceeds a user-specified threshold. This measure yields a much better assessment of the association strength of the items than simple support. Since the (generalized) Jaccard index is, like the support, also anti-monotone, the same basic approach can be used for the search, provided it is extended to compute the denominator of the Jaccard index.

In addition to the (generalized) Jaccard index, this program offers a large variety of other (generalized) similarity measures, which may also be used to find item sets based on cover similarity, including the measures defined by by Kulczynski, Dice, Sokal & Sneath, Sokal & Michener, Faith, Roger & Tanimoto etc. All of these measures can also be shown to be anti-monotone.

The algorithm used in this program is described in the following paper:

• Item Set Mining Based on Cover Similarity
  Marc Segond and Christian Borgelt
  Proc. 15th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD 2011, Shenzhen, China), LNCS 6635:493-505
  Springer-Verlag, Berlin, Germany 2011
  pakdd_11.pdf (415 kb) pakdd_11.ps.gz (351 kb) (12 pages)

More information about frequent item set mining, implementations of other algorithms as well as test data sets can be found at the Frequent Itemset Mining Implementations Repository.