IJIRCSTJournal International Journal of Innovative Research in Computer Science and Technology I S Volume 1 Issue 1 Computer Science & Engineering English September - October 2013 N N Y 2019 11 09 Computer Sciences Itemset Mining over Large Transactional Tables on the Relational Databases English Y 6 11 Arun Pratap Srivastava English Y Prof.(Dr) Mohd. Hussain English N Most of the itemset mining approaches are memory-like and run outside of the database. On the other hand, when we deal with data warehouse the size of tables is extremely huge for memory copy. In addition, using a pure SQL-like approach is quite inefficient. Actually, those implementations rarely take advantages of database programming. Furthermore, RDBMS vendors offer a lot of features for taking control and management of the data. We purpose a pattern growth mining approach by means of database programming for finding all frequent itemsets. The main idea is to avoid one-at-a-time record retrieval from the database, saving both the copying and process context switching, expensive joins, and table reconstruction. The empirical evaluation of our approach shows that runs competitively with the most known itemset mining implementations based on SQL. Our performance evaluation was made with SQL Server 2000 (v.8) and T-SQL, throughout several synthetical datasets. English SQL, RDBMS, Mining, Itemset, OLAP https://ijircst.org/abstract.php?article_id=5