An enhanced constraint based technique for frequent itemset mining in transactional databases

Authors

  • Ramah Sivakumar
  • Dr J.G.R. Sathiaseelan

DOI:

https://doi.org/10.14419/ijet.v7i2.22.11807

Published:

2018-04-20

Keywords:

Itemset, FPGrowth algorithm, Frequent Patterns, support.

Abstract

Mining frequent patterns is one of the wide area of research in recent times as it has numerous social applications.  Variety of frequent patterns finds usage in diverse applications and the research to mine those in an optimized way is an important aspect under consideration.  So far, many algorithms had been proposed for mining frequent itemsets and each has their own pros and cons.  The basic algorithms used in the process are Apriori, Fpgrowth and Eclat.  Many enhancements of these algorithms are ongoing process in recent times.  In this paper, an enhanced Varied Support Frequent Itemset (VSFIM) algorithm is proposed which is an enhancement of FPGrowth algorithm. Unique minimum support for each item in the transaction is provided and then mining is done in the proposed approach.   The performance of the proposed algorithm is tested with existing algorithms.  It is found that VSFIM outperformed the existing algorithms in both processing time and space utilization.

 

 

References

[1] M.S. Chen, J. Han, P.S. Yu, “Data mining: an overview from a database perspectiveâ€, IEEE Transactions on Knowledge and Data Engineering, 1996, 8, pp. 866-883.

[2] J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher, San Francisco, CA, USA, 2001.

[3] Jian Pei, Jiawei Han, Hongjun Lu,, ShojiroNishio, Shiwei Tangand DongqingYang,“H-Mine: Fast and space-preserving frequent pattern mining in large databasesâ€, IIE Transactions (2007) 39, 593–605

[4] Christian Borgelt,â€Simple Algorithms for Frequent Item Set Miningâ€, Advances in Machine Learning II pp 351-369

[5] KaramGoudaEmailauthorMohammed J. Zaki, “GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsetsâ€, Data Mining and Knowledge Discovery November 2005, Volume 11, Issue 3, pp 223–242

[6] Dao-I Lin ; Z.M. Kedem,“Pincer-search: an efficient algorithm for discovering the maximum frequent setâ€, IEEE Transactions on Knowledge and Data Engineering ( Volume: 14, Issue: 3, May/Jun 2002 )

[7] Tahrima Hashem a, Md. Rezaul Karim a, Md. Samiullah a, Chowdhury Farhan Ahmed, “An Efficient Dynamic Superset Bit-Vector Approach for Mining Frequent Closed Itemsets and their Lattice Structureâ€, Elsevier,September 22, 2016

[8] Huong Bui a , Bay Vo, Ham Nguyen d , Tu-Anh Nguyen-Hoang , Tzung-Pei Hong, “A weighted N-list-based method for mining frequent weighted itemsetsâ€, Expert Systems With Applications(2017), https://doi.org/10.1016/j.eswa.2017.10.039https://doi.org/10.1016/

[9] Bay Vo ,Sang Pham, Tuong Le ,Zhi-Hong Deng, “A novel approach for mining maximalfrequent patternsâ€,http://dx.doi.org/10.1016/j.eswa.2016.12.0230957-4174/©2016ElsevierLtd.

[10] Md. RezaulKarima,, Michael Cocheza, , OyaDenizBeyanb, Chowdhury Farhan Ahmed, Stefan Deckera, “Mining Maximal Frequent Patterns in Transactional Databases and DynamicData Streams: a Spark-based Approachâ€, Information Sciences, December 1, 2017

[11] Ramah Sivakumar, J.G.R.Sathiaseelan, “A Performance based Empirical Study of the Frequent Itemset Mining Algorithmsâ€, International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017), IEEE Page 350-355

[12] Ramah Sivakumar, J.G.R.Sathiaseelan, “A hybrid algorithm for mining frequent itemsets in transactional databasesâ€,International Conference on Recent Advances In Computing And Communication 2018, In Print.

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How to Cite

Sivakumar, R., & J.G.R. Sathiaseelan, D. (2018). An enhanced constraint based technique for frequent itemset mining in transactional databases. International Journal of Engineering & Technology, 7(2.22), 45–48. https://doi.org/10.14419/ijet.v7i2.22.11807
Received 2018-04-20
Accepted 2018-04-20
Published 2018-04-20