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
Agarwal, R., Shim., R.: Developing tightly-coupled data mining application on a relational database system. In Proc.of the 2nd Int. Conf. on Knowledge Discovery in Database and Data Mining, Portland, Oregon (1996)
Agrawal, R., Imielinski, T., Swami, A..: Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (1993) 207–216
Agrawal, R., Srikant., R.: Fast algorithms for mining association rules. In Proc. of the 20thVery Large Data Base Conference (1994) 487–499
Alves, R., Belo, O.: Integrating Pattern Growth Mining on SQL-Server RDBMS. Technical Report-003, University of Minho, Department of Informatics, May (2005) http://alfa.di.uminho.pt/~ronnie/files_files/rt/2005-RT3-Ronnie.pdf
Alves, R., Gabriel, P., Azevedo, P., Belo, O.: A Hybrid Method to Discover Inter- Transactional Rules. In Proceedings of the JISBD’2005, Granada (2005)
Cheung, W., Zaïane, O. R.: Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint, Constraint, Seventh International Database Engineering and Applications Symposium (IDEAS 2003), Hong Kong, China, July 16-18 (2003) 111-116
El-Hajj, M., Zaïane, O.R.: Inverted Matrix: Efficient Discovery of Frequent Items in Large Datasets in the Context of Interactive Mining, in Proc. 2003 Int'l Conf. on Knowledge Discovery and Data Mining (ACM SIGKDD), Washington, DC, USA, August 24-27 (2003) 109-118
Han, J., Pei, J., Yin., Y.: Mining frequent patterns without candidate generation. In Proc. of ACM SIGMOD Intl. Conference on Management of Data, (2000) 1–12.
Hidber, C.: Online association rule mining. In A. Delis, C. Faloutsos, and S.Ghandeharizadeh, editors, Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, volume 28(2) of SIGMOD Record. ACM Press (1999) 145–156
Orlando, S., Palmerini, P., Perego, R.: Enhancing the apriori algorithm for frequent set counting. In Y. Kambayashi, W. Winiwarter, and M. Arikawa, editors, Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery, volume 2114 of Lecture Notes in Computer Science (2001) 71–82
Orlando, S., Palmerini, P., Perego, R., Silvestri, F.: Adaptive and resource-aware mining of frequent sets. In V. Kumar, S. Tsumoto, P.S. Yu, and N.Zhong, editors, Proceedings of the 2002 IEEE International Conference on Data Mining. IEEE Computer Society (2002)
Rantzau, R.: Processing frequent itemset discovery queries by division and set containment join operators. In DMKD03: 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2003)
Sarawagi, S., Thomas, S., Agrawal, R.: Integrating mining with relational database systems: alternatives and implications. In Proc. of the ACM SIGMOD Conference on Management of data, Seattle, Washington, USA (1998)
Shang, X., Sattler, K., Geist, I.: Sql based frequent pattern mining without candidate generation. In SAC’04 Data Mining, Nicosia, Cyprus (2004)
Wang, H., Zaniolo, C.: Using SQL to build new aggregates and extenders for Object- Relational systems. In Proc. Of the 26th Int. Conf. on Very Large Databases, Cairo, Egypt (2000)
Yoshizawa, T., Pramudiono, I., Kitsuregawa, M.: Sql based association rule mining using commercial rdbms (ibm db2 udb eee). In In Proc. DaWaK, London, UK (2000)
[Arun Pratap Srivastava, Prof.(Dr) Mohd. Hussain (2013), Itemset Mining over Large Transactional Tables on the Relational Databases, International Journal of Innovative Research in Computer Science & Technology (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)
