Proposed algorithm for extracting association rule depend on closed frequent itemset (EACFI)‎

Abstract EN

Association rules are important one of data mining activities.

All algorithms of association rule mining consist of finding frequency of item sets, which satisfy a minimum support threshold, and then compute confidence percentage for each k-item sets to construct strong association rules’.

Some of these association rules are not important because the extracted knowledge from their is found in other.

Hence we proposed algorithm to construct only important association rules by depend on closed frequent item set.

An item set is closed if one of its immediate supersets has the same support as the item set.

Finding these closed frequent item sets can be of a great help to purge a lot of item sets that are not needed to find association rules.

So that aid to reduce execution time and work space of algorithm and end the algorithm at any level of k-item set, without construct all K-item set.