An Efficient Algorithm for Mining Association Rules Using Confident Frequent Itemsets

Identifying frequent item sets is one of the most important issues faced by the knowledge discovery and data mining community. There have been a number of successful algorithms developed for extracting frequent item sets in very large databases. Frequent item set mining leads to the discovery of associations and correlations among items in large transactional or relational datasets. A problem with such a process is that the solution of interesting patterns has to be performed only on frequent item sets. Pushing constraints in frequent item sets mining can help pruning the search space. In this paper, an efficient algorithm is proposed to integrate confidence measure during the process of mining frequent item sets, which generates confident frequent item sets. Consequently, the suggested algorithm generates strong association rules from these confident frequent item sets. This technique has been implemented and the experimental results show the usefulness and effectiveness of the proposed algorithm.