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

  • Authors

    • Ramah Sivakumar
    • Dr J.G.R. Sathiaseelan
    https://doi.org/10.14419/ijet.v7i2.22.11807

    Received date: April 20, 2018

    Accepted date: April 20, 2018

    Published date: April 20, 2018

  • 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 perspective”, 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 Tan-gand DongqingYang,“H-Mine: Fast and space-preserving fre-quent pattern mining in large databases”, IIE Transactions (2007) 39, 593–605
    4. Christian Borgelt,”Simple Algorithms for Frequent Item Set Min-ing”, Advances in Machine Learning II pp 351-369
    5. KaramGoudaEmailauthorMohammed J. Zaki, “GenMax: An Ef-ficient Algorithm for Mining Maximal Frequent Itemsets”, 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 set”, 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 Structure”, 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 itemsets”, Expert Systems With Applica-tions(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 ap-proach for mining maximalfrequent pat-terns”,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 Approach”, Information Sciences, De-cember 1, 2017
    11. Ramah Sivakumar, J.G.R.Sathiaseelan, “A Performance based Empirical Study of the Frequent Itemset Mining Algorithms”, In-ternational Conference on Power, Control, Signals and Instru-mentation Engineering (ICPCSI-2017), IEEE Page 350-355
    12. Ramah Sivakumar, J.G.R.Sathiaseelan, “A hybrid algorithm for mining frequent itemsets in transactional databases”,International Conference on Recent Advances In Computing And Communi-cation 2018, In Print.

  • Downloads

  • 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 and Technology, 7(2.22), 45-48. https://doi.org/10.14419/ijet.v7i2.22.11807