Association Rule Integrasi Pendekatan Metode Custom Hashing dan Data Partitioning untuk Mempercepat Proses Pencarian Frekuensi Item-set pada Algoritma Apriori
DOI:
https://doi.org/10.30812/matrik.v20i1.833Keywords:
Apriori, Aturan Asosiasi, FP-Growth, Hashing, Data PartisiAbstract
Data mining dengan peran asosiasi sudah banyak digunakan oleh dunia usaha, salah satu algoritma yang sering digunakan untuk aturan asosiasi adalah apriori. Namun apriori memiliki kelemahan dalam hal performa, karena pada setiap penentuan frequent k-itemset harus melakukan scan database. Hal ini akan menjadi masalah apabila kandidat k-itemset memiliki dimensi yang banyak. proses scan database yang besar akan memakan waktu yang lama dan berpengaruh pada penggunaan memori dan prosesor. Apriori sudah sering dikembangkan, salah satu yang populer adalah Frequent Pattern (fp-growth), apriori dan fp-growth sama-sama merupakan algoritma untuk aturan asosiasi, hanya saja fp-growth menggunakan pendekatan yang berbeda dengan apriori yakni menggunakan pendekatan Frequent Pattern Tree (fp-tree). Meski fp-growth memiiki performa yang bagus ketika scan database namun rules yang di hasilkan oleh fp-growth tidak sebaik yang di hasilkan oleh apriori. Alternatif lain yang bisa digunakan adalah metode hashing, hal ini bisa menjadi solusi untuk mengatasi masalah dalam proses pencarian dan penentuan frequent k-itemset, sehingga proses scan database bisa lebih cepat. Tujuan penelitian adalah memperbaiki kinerja apriori dalam proses pencarian frekuensi itemset sehingga waktu scan database bisa lebih cepat
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References
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[2] R. M. Chezian and K. S. Kumar, “A Survey on Association Rule Mining using Apriori Algorithm,†International Journal of Computer Applications, vol. 45, no. 5, 2012.
[3] M. North, Data Mining for the Messes. Utah: CreateSpace Independent Publishing Platform, 2016.
[4] L. Jian-ping, W. Ying, and Y. Fan-ding, “Incremental Mining Alogorithm Pre-FP in Association Rules Based on FP-tree,†in First International Conference on Networking and Distributed Computing, 2010, pp. 199–203.
[5] J. Pieprzyk and B. Sadeghiyan, Design of Hashing Algorithms. Berlin: Springer Berlin Heidelberg, 2006.
[6] V. Kashyp and A. K. Gupta, “A Review on Modern Approach: New Parameter for Recent Improvement of Apriori Algorithm,†International Journal of Computer Science and Mobile Computing, vol. 4, no. 12, 2015.
[7] C. C. Aggarwal, Data Mining: The Textbook. Switzerland: Springer, 2015.
[8] J. Han and M. Kamber, Data Mining: Concepts and Techniques, 2nd ed. San Fransisco: Elsevier Science & Technology, 2006.
[9] D. T. Larose, Data Mining Method and Model. Canada: Wiley, 2007.
[10] V. Kotu and B. Deshpande, Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Burlington: Morgan Kaufmann, 2014.
[11] R. Rathinasabapathy and R. Bhaskaran, “Performance Comparison of Hashing Algorithm with Apriori,†in International Conference on Advances in Computing, Control, and Telecommunication Technologies, 2009, pp. 729–733.
[12] J. S. Park, M.-S. Chen, and P. S. Yu, “An effective hash-based algorithm for mining association rules,†ACM SIGMOD Record, vol. 24, no. 2, pp. 175–186, May 1995.
[13] A. Bhandari, A. Gupta, and D. Das, “Improvised Apriori Algorithm Using Frequent Pattern Tree for Real Time Applications in Data Mining,†Procedia Computer Science, vol. 46, pp. 644–651, 2015.
[14] B. M. Rao and S. Aguru, “A Hash Based Frequent Itemset Mining using Rehashing,†International Journal on Recent and Innovation Trends in Computing and Communication, vol. 2, no. 12, 2014.
[15] A. M. J. M. Z. Rahman, P. Balasubramanie, and P. V. Krihsna, “A Hash based Mining Algorithm for Maximal Frequent Item Sets using Linear Probing,†Journal of Computer Science, vol. 8, no. 1, pp. 14–19, 2009.
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Published
2020-09-29
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Section
Articles
How to Cite
[1]
M. Syahrir and F. Fatimatuzzahra, “Association Rule Integrasi Pendekatan Metode Custom Hashing dan Data Partitioning untuk Mempercepat Proses Pencarian Frekuensi Item-set pada Algoritma Apriori”, MATRIK, vol. 20, no. 1, pp. 149–158, Sep. 2020, doi: 10.30812/matrik.v20i1.833.
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