An Efficient Algorithm for Mining Frequent Itemsets from Compressed Transactions using Matrix Approach

Authors

  • Ameta G Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India 2 Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India
  • Bhatnagar D Department of Computer Science & Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India

Keywords:

Mining Frequent Pattern, Matrix Approach, Reference Matrix, Compressed Database, Market Basket Analysis, Apriori Algorithm

Abstract

Mining of frequent itemsets from large databases has been an interesting area for data miners from the beginning of data mining research. Knowing frequent patterns, data miners can determine interesting relationships among the items. In the proposed work, the original database is scanned once and the encoded database transactions are stored as a matrix. All frequent patterns are then determined from this matrix of coded transactions. An efficient algorithm has been developed to mine all frequent itemsets directly from this encoded matrix with the help of a reference matrix. The proposed approach reduces the memory size required for the database and the number of database scans to one. The algorithm finds its application in distributed data mining and secure data publishing.

References

. Al-Maolegi M., Arkok B., “An improved Apriori Algorithm for Association Rules”, International Journal on Natural Language Computing, Vol. 3(1), pp. 21-29, 2014.

. Yabing J., “Research of an Improved Apriori Algorithm in Data Mining Association Rules”, International Journal of Computation and Communication Engineering, Vol. 2(1), pp. 25-27, 2013.

. Rehab A., Alva H., Anasuya B., Patil V., “New Matrix based approach to improve Apriori Algorithm”, International Journal of Computer Science & Network Solutions, Vol. 1(4), pp. 102-109, 2013.

. Liu Z., Sun T., Sang G., “An Algorithm of Association Rules Mining in Large Databases Based on Sampling”, International Journal of Database Theory and Application, Vol. 6(6), pp. 95-104, 2013.

. Al-Shoreman H., Jbara Y., “An Efficient Algorithm for Mining Association Rules for Large Itemsets in Large Databases”, International Journal of Engineering and Innovative Technology, Vol. 3(10), pp. 237-240, 2014.

. Kotsiantis S., Kanellopoulos D., “Association Rule Mining: A Recent Overview, GESTS”, International Transactions on Computer Science and Engineering, Vol. 32(1) , pp. 71-82, 2006.

. Tanna P., Ghodasara Y., “Using Apriori with WEKA for frequent Pattern Mining”, International Journal of Engineering Trends and Technology, Vol. 12(3), pp. 127-134, 2014.

. Mishra S., Pattanaik S., Patnaik D., “Application of association rules to determine item sets from large databases”, International Journal of Computer Science Engineering, Vol. 2(6), pp. 276-278, 2013.

. Han J., Pei J., Yin Y., “Mining Frequent Patterns without Candidate Generation ACM SIGMOD”, International Conference on Management of Data, pp. 1-12, 2000.

. Jain S., Dave M., Agrawal A., “Cost Vector Matrix – A new approach to Association Rule Mining”, International Journal of Recent research and Review, Vol. 7(2), pp. 68-73, 2014.

. Choubey A., Patel R., Rana J., “Graph based new approach for frequent pattern mining”, International Journal of Computer Science and Information Technology, Vol. 4(1), pp. 221-235, 2012.

. Grun B., Hahsler M.., “arules- A Computational Environment for Mining Association Rules and Frequent Item Sets”, Journal of Statistical Software, Vol. 14(15), pp. 1-23, 2005.

. Rao C., Babu D., Shankar R., Kumar V., Rajanikanth J., Shekhar C., “Mining Association Rules Based on Boolean Algorithm – a Study in Large Databases”, International Journal of Machine Learning and Computing Vol. 3(4), pp. 347-351, 2013.

. Ezhilvathani A., Raja K., “Implementation of Parallel Apriori Algorithm on Hadoop Cluster”, International Journal of Computer Science and Mobile Computing, Vol. 2(4), pp. 513-516, 2013.

. Kumar D., Jayaveeran N., “ A Survey on Association Rule Mining Algorithms for Frequent Itemsets”, International Journal of Computer Sciences and Engineering, Vol. 4(10), pp. 120-125, 2016.

. Mani K., Akila R., “Enhancing the Performance in Generating Association Rules using Singleton Apriori”, International Journal of Information Technology and Computer Science, Vol. 9(1), pp. 58-64, 2017.

Downloads

Published

2025-11-11

How to Cite

[1]
G. Ameta and D. Bhatnagar, “An Efficient Algorithm for Mining Frequent Itemsets from Compressed Transactions using Matrix Approach”, Int. J. Comp. Sci. Eng., vol. 5, no. 2, pp. 46–50, Nov. 2025.

Issue

Vol. 5 No. 2 (2017): IJCSE February Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.