Research Article
Performance Evaluation of the PML Algorithm based on Memory Usage and Execution Time
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 187 - Issue 117 |
| Published: June 2026 |
| Authors: Surati Sandipkumar B., Desai Apurva A. |
10.5120/ijca648ae6370891
|
Surati Sandipkumar B., Desai Apurva A. . Performance Evaluation of the PML Algorithm based on Memory Usage and Execution Time. International Journal of Computer Applications. 187, 117 (June 2026), 28-34. DOI=10.5120/ijca648ae6370891
@article{ 10.5120/ijca648ae6370891,
author = { Surati Sandipkumar B.,Desai Apurva A. },
title = { Performance Evaluation of the PML Algorithm based on Memory Usage and Execution Time },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 117 },
pages = { 28-34 },
doi = { 10.5120/ijca648ae6370891 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Surati Sandipkumar B.
%A Desai Apurva A.
%T Performance Evaluation of the PML Algorithm based on Memory Usage and Execution Time%T
%J International Journal of Computer Applications
%V 187
%N 117
%P 28-34
%R 10.5120/ijca648ae6370891
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Frequent pattern generation from transaction datasets is an important issue. This can be achieved through frequent pattern mining. There are many popular algorithms, such as Apriori, FP-Growth, and ECLAT, that are widely used for frequent pattern generation. In this paper, we discuss an innovative frequent pattern mining algorithm using a Linked List (PML). This paper also analyzes the performance of PML algorithm in comparison with other popular algorithms in terms of memory usage and time consumption for generating frequent patterns.
References
- R. Agrawal, T.Imielinski and A. Swami, 1993. Mining Association Rules between set of items in large database.Proceeding of the 1993 ACM SIGMOD Conference Washington DC, USA, May 1993.
- J. Han, H. Cheng, D. Xin, and X. Yan, 2007. Frequent Frequent pattern mining: current status and future directions, Data Mining Knowledge Dis(2007), Springer Science+Business Media, LLC 2007.
- S. Surati, A. Desai, 2017, Pattern Mining Using Linked List (PML), 8th Internation Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, 2017.
- R. Agrawal and R. Srikant, 1994. Fast Algorithms for Mining Association Rules. Proc. 20th Int. Conf. on very Large Databases (VLDB 1994, Santiago de Chile), 487–499. Morgan Kaufmann, San Mateo, CA, USA 1994
- J. Han, J. Pei, Y. Yin and R. Mao, 2004. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining Knowledge Discovery, 8: 53-87.
- M.J. Zaki, 2000. Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng., 12: 372-390.
- S. Chaudhari, M. Borkhatariya, A. Churi and M. Bhonsle, 2016. Implementation and Analysis of Improved Apriori Algorithm, International Journal of Engineering Science and Innovative Technology (IJESIT) Vol. 5, Issue 2, March 2016.
- J. Du, X. Zhang and H. Zhang, 2016. Research and improvement of Apriori algorithm, 6th Internation conference on information science and technology (ICIST), May 2016.
- J. Gudkha, A. Patel and S. More, 2017. Compressed Frequent Pattern Tree, International Journal of Engineering Sciences & Research Technology, April, 2017.
- S. Bhise, Prof. S. Kale, 2017. Efficient Algorithms to find Frequent Itemset Using Data Mining, International Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 06, June 2017.
- X.Yu and H. Wang, 2014. Improvement of ECLAT algorithm based on Support in Frequent Itemset Mining, Journal of Computers, Vol. 9, No. 9, September 2014.
- Z. Ma, J. Yang, T. Zhang and F. Liu, 2016. An Improved ECLAT algorithm for Mining Associaton Rules based on Increased Search Strategy, International Journal of Database Theory and Application, Vol. 9, No. 5 (2016).
- S. Surati., A. Desai, Pattern Mining (PM) Algorithm, 2012. VNSGU Journal of Science & Technology, Vol. 3, Issue 2, March 2012.
- D. Gunopulos, H. Mannila, and S. Saluja,1997. Discovering all the most specific sentences by randomized algorithms. In Intl. Conf. on Database Theory, January 1997.
Index Terms
Keywords