Itemset Mining over Large Transactional Tables on the Relational Databases
Arun Pratap Srivastava , Prof.(Dr) Mohd. Hussain
Abstract
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.
SQL, RDBMS, Mining, Itemset, OLAP
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[Arun Pratap Srivastava , Prof.(Dr) Mohd. Hussain (2013) Itemset Mining over Large Transactional Tables on the Relational Databases IJIRCST Vol-1 Issue-1 Page No-6-11] (ISSN 2347 - 5552). www.ijircst.org
Arun Pratap Srivastava
Ph.D. Student, NIMS University, Jaipur, India,
(e-mail: arun019@yahoo.com)
