Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

Adie Wahyudi Oktavia Gama, Ni Made Widnyani

Abstract


Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.

Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;


Full Text:

PDF

References


U. Fayyad, G. P. Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Mag., vol. 17, no. 3, pp. 37–54, 1996.

P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. United States of America: Pearson Addison-Wesley, 2006.

J. Pamungkas and Y. Handrianto, “Assosiation Rules for Product Sales Data Analysis Using The Apriori Algorithm,” Sink. Junal Penelit. Tek. Inform., vol. 5, no. 1, p. 84, 2020.

R. Agrawal, “Mining Association Rules between Sets of Items in Large Databases,” in Proceeding of the 1993 ACM SIGMOD Conference Washington DC, USA, 1993, pp. 1–10.

R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceeding 20th VLDB Conf. Santiago, Chile., 1994.

J. Han and M. Kamber, Data Mining: Concepts and Techniques Second Edition. United States of America: Elsevier Inc., 2006.

L. F. Panjaitan, Y. Handrianto, and A. Nurhadi, “Apriori Algorithm On Car Rental Analysis With The Most Popular Brands,” Sink. Junal Penelit. Tek. Inform., vol. 4, no. 2, p. 47, 2020.

E. Irfiani, “Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores,” Sink. Junal Penelit. Tek. Inform., vol. 3, no. 2, p. 218, 2019.

G. Danon, M. Schneider, M. Last, M. Litvak, and A. Kandel, “An Apriori-like algorithm for Extracting Fuzzy Association Rules between Keyphrases in Text Documents,” Cs.Bgu.Ac.Il, 2006.

Luthfiah and K. Ditha Tania, “K-Means and apriori algorithm for pharmaceutical care medicine (case study: Eye hospital of South Sumatera Province),” in Journal of Physics: Conference Series, 2019, pp. 1–7.

A. Ezhilvathani and K. Raja, “Implementation of Parallel Apriori Algorithm on Hadoop Cluster,” Int. J. Comput. Sci. Mob. Comput., vol. 2, no. 4, pp. 513–516, 2013.

N. A. Harun, M. Makhtar, A. A. Aziz, Z. A. Zakaria, F. S. Abdullah, and J. A. Jusoh, “The application of Apriori algorithm in predicting flood areas,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 3, pp. 763–769, 2017.

N. Badal and S. Tripathi, “Frequent Data Itemset Mining Using VS _ Apriori Algorithms,” Int. J. Comput. Sci. Eng., vol. 2, no. 4, pp. 1111–1118, 2010.

J. Suresh and T. Ramanjaneyulu, “Mining Frequent Itemsets Using Apriori Algorithm,” Int. J. Comput. Trends Technol., vol. 4, no. 4, pp. 760–764, 2013.

S. A. Abaya, “Association Rule Mining based on Apriori Algorithm in Minimizing Candidate Generation,” Int. J. Sci. Eng. Res., vol. 3, no. 7, pp. 1–4, 2012.

J. Yabing, “Research of an Improved Apriori Algorithm in Data Mining Association Rules,” Int. J. Comput. Commun. Eng., vol. 2, no. 1, pp. 25–27, 2013.

J. Singh, H. Ram, and J. S. Sodhi, “Improving Efficiency of Apriori Algorithm Using Transaction Reduction,” Int. J. Sci. Res. Publ., vol. 3, no. 1, pp. 1–4, 2013.

J. Silva, N. Varela, L. A. B. López, and R. H. R. Millán, “Association rules extraction for customer segmentation in the SMES sector using the apriori algorithm,” in Procedia Computer Science, 2019, pp. 1207–1212.

A. W. O. Gama, I. K. G. D. Putra, and I. P. A. Bayupati, “Implementasi Algoritma Apriori untuk Menemukan Frequent Itemset dalam Keranjang Belanja,” Tekonologi Elektro, vol. 15, no. 2, pp. 27–32, 2016.

K. K. Widiartha, D. Putu, and D. Kumala, “Shopping Cart Analysis System in Product Layout Management with Apriori Algorithm,” Int. J. Appl. Comput. Sci. Inform. Eng., vol. 1, no. 2, pp. 53–64, 2019.

K. S. Raju, A. D. Devi, and D. D. D. Suribabu, “Mining Frequent Item Sets Using Apriori Algorithm on Shopping Dataset,” Mukth Shabd J., vol. 9, no. 5, pp. 6309–6320, 2020.

B. Patel, V. K. Chaudhari, R. K. Karan, and Y. . Rana, “Optimization of Association Rule Mining Apriori Algorithm Using ACO,” Int. J. Soft Comput. Eng., vol. 1, no. 1, pp. 24–26, 2011.

M. F. Akas, A. G. M. Zaman, and A. Khan, “Combined item sets generation using modified apriori algorithm,” in ACM International Conference Proceeding Series, 2020, pp. 4–6.

H. Yu, J. Wen, H. Wang, and J. Li, “An improved Apriori algorithm based on the Boolean matrix and Hadoop,” Procedia Eng., vol. 15, pp. 1827–1831, 2011.

Z. Jie and W. Gang, “Intelligence Data Mining Based on Improved Apriori Algorithm,” J. Comput., vol. 14, no. 1, pp. 52–62, 2019.

X. Liu, Y. Zhao, and M. Sun, “An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors,” Discret. Dyn. Nat. Soc., vol. 2017, 2017.

R. Sun and Y. Li, “Applying Prefixed-Itemset and Compression Matrix to Optimize the MapReduce-based Apriori Algorithm on Hadoop,” in ACM International Conference Proceeding Series, 2020, pp. 89–93.

X. Yuan, “An improved Apriori algorithm for mining association rules,” in AIP Conference Proceedings, 2017, pp. 1–6.

D. T. Larose, An introduction to data mining, vol. 134. Canada: John Wiley & Sons, Inc, 2005.

Y. Kurnia, Y. Isharianto, Y. C. Giap, A. Hermawan, and Riki, “Study of application of data mining market basket analysis for knowing sales pattern (association of items) at the O! Fish restaurant using apriori algorithm,” in Journal of Physics: Conference Series, 2019, pp. 1–6.

J. R. Delos Arcos and A. A. Hernandez, “Analyzing online transaction data using association rule mining: Misumi philippines market basket analysis,” in ACM International Conference Proceeding Series, 2019, pp. 45–49.




DOI: http://dx.doi.org/10.17977/um018v3i22020p89-98

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Knowledge Engineering and Data Science

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

Flag Counter

Creative Commons License


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

View My Stats