Performance Comparison of Apriori and FP-Growth Techniques in Generating Association Rules to Prostate Cancer

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Jaree Thongkam
Vatinee Sukmak
Phimaphot Sukmak


Currently, the incidence of prostate cancer has been increasing around the world. Knowing prostate cancer survival time is very important for physicians and patients, since physicians can guide decision making in order to select the proper treatment that maximize benefit for each patient. This study aimed to compare Apriori and FP-Growth techniques in generating association rules to prostate cancer. The data were collected from SEER between January 2004 and 2014with the final 2,308 records. Apriori and FP-Growth techniques were used. The results showed that the FP-Growth technique has the ability to build more association rules than Apriori technique. The confidence of FP-Growth is 96.00% with 80-84.9% of support which is better than Apriori.


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Thongkam, J., Sukmak, V., & Sukmak, P. (2019). Performance Comparison of Apriori and FP-Growth Techniques in Generating Association Rules to Prostate Cancer. Journal of Applied Informatics and Technology, 1(2), 103–111.
Research Article


Agrawal, A., and Choudhary, A. (2011). Identifying HotSpots in lung cancer data using association rule mining. Proceedings of the IEEE 11th International Conference on Data Mining Workshops, p. 995-1002.

Anand, R. V., and Dinakaran, M. (2017). Handling stakeholder conflict by agile requirement prioritization using Apriori technique. Computers & Electrical Engineering, 61, 126-136.

Arifin, D. D., Shaufiah, and Bijaksana, M. A. (2016). Enhancing spam detection on mobile phone Short Message Service (SMS) performance using FP-growth and Naive Bayes Classifier. Proceedings of the IEEE Asia Pacific Conference on Wireless and Mobile, p. 80-84.

Chun-Sheng, Z., and Yan, L. (2014). Extension of local association rules mining algorithm based on apriori algorithm. Proceedings of the IEEE 5th International Conference on Software Engineering and Service Science, p. 340-343.

Creighton, C., and Hanash, S. (2003). Mining gene expression databases for association rules. Bioinformatics, 19, 79-86.

Deng, Z. H., and Lv, S.-L. (2014). Fast mining frequent itemsets using Nodesets. Expert Systems with Applications, 41(10), 4505–4512.

Fahrudin, T. M., Syarif, I., and Barakbah, A. R. (2017). Discovering patterns of NED-breast cancer based on association rules using apriori and FP-growth. Proceedings of the International Electronics Symposium on Knowledge Creation and Intelligent Computing, p. 132-139.

Fan, Q., Zhu, C., Xiao, J., Wang, B., Yin, L., Xu, X., and Rong, F. (2010). An application of Apriori algorithm in SEER breast cancer data. Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, p. 114-116.

Han, J., J.Pei, and Yin, Y. (2000). Mining frequent patterns without candidate generation. International Conference on Management of Data, p. 1-12.

Han, J. w., and Kamber, M. (2006). Data miming concepts and techniques. New York: Morgan Kaufmann.

Jiang, S., Pan, X., Xiao, Y., Zhou, Q., Li, M., and Yang, J. (2017). Identification of vulnerable lines in large power grid based on FP-growth algorithm. The Journal of Engineering, 2017(13), 1862-1866.

Liu, B., Hsu, W., and Ma, Y. (1998). Integrating classification and association rule mining. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, p. 80-86.

Mariana, S., Surjandari, I., Dhini, A., Rosyidah, A., and Prameswari, P. (2017). Association rule mining for building book recommendation system in online public access catalog. Proceedings of the 3rd International Conference on Science in Information Technology, p. 246-250.

Pianprasit, P., Seesai, P., and Rimcharoen, S. (2017). Association rule mining for analyzing placement test of computer science students. Proceedings of the 2nd International Conference on Information Technology, p. 1-5.

Pinheiro, F., Kuo, M., Thomo, A., and Barnett, J. (2013). Extracting association rules from liver cancer data using the FP-growth algorithm. Proceedings of the IEEE 3rd International Conference on Computational Advances in Bio and medical Sciences, p. 1-1.

Putten, v., Vos-Geelen, d., Nieuwenhuijzen, G., Siersema, P., Lemmens, V., Rosman, C., van der Sangen, M., and Verhoeven, R. (2018). Long-term survival improvement in oesophageal cancer in the Netherlands. European Journal of Cancer, 94, 138-147.

Singh, S., Garg, R., and Mishra, P. K. (2018). Performance optimization of MapReduce-based Apriori algorithm on Hadoop cluster. Computers & Electrical Engineering, 67, 348-364.

Society, A. C. (2018). Survival rates for prostate cancer. Retrived Date: 12/01/2018, Retrived from:

Wankhede, S. S., Armstrong, L. J., and Gandhi, N. (2017). Characterising the influence of drought on Jowar crop production in India using association rule mining. Proceedings of the IEEE Technological Innovations in ICT for Agriculture and Rural Development, p. 57-63.

Yang, J., Huang, H., and Jin, X. (2017). Mining web access sequence with improved apriori algorithm. Proceedings of the IEEE International Conference on Computational Science and Engineering and IEEE International Conference on Embedded and Ubiquitous Computing, p. 780-784.

Yunlong, S., and Ran, W. (2011). Research on application of data mining based on FP-growth algorithm for digital library. Proceedings of the 2nd International Conference on Mechanic Automation and Control Engineering, p. 1525-1528.

Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390.

Zhang, T., Yin, C., and Pan, L. (2017). Improved clustering and association rules mining for university student course scores. Proceedings of the 12th International Conference on Intelligent Systems and Knowledge Engineering, p. 1-6.

พวงทอง ไกรพิบูลย์ และคณะ (2014). มะเร็งต่อมลูกหมาก. สีบค้นเมื่อ: 01/02/2018, สืบค้นจาก:มะเร็งต่อมลูกหมาก/

พัฒนพงษ์ ดลรัตน์ และจารี ทองคำ (2560). การเปรียบเทียบเทคนิคเหมืองข้อมูลในการพยากรณ์ความสำเร็จการศึกษาของนักเรียน ระดับประกาศนียบัตรวิชาชีพ, การประชุมทางวิชาการระดับชาติ NCCIT ครั้งที่ 13, หน้า. 8-13.

ไพโรจน์ อภัยบัณฑิตกุล. (2559). ความรู้เพื่อสุขภาพ มะเร็งต่อมลูกหมาก. สีบค้นเมื่อ: 17/08/2561, สืบค้นจาก: https://www.