Sensitivity Analysis of Non-Dominated Sorting Genetic Algorithm-II for Quantitative Association Rules Mining

Year 2020, , 37 - 46, 31.01.2020

Elif Varol Altay Bilal Alatas

https://doi.org/10.17671/gazibtd.503349

Abstract

There are many association rules mining studies that focus on datasets with binary or discrete values. However, the data in real-world applications are generally composed of quantitative values. In association rules discovered within quantitative data, it is very hard to determine which attributes will be included in the rules to be discovered and which ones will be on the left of the rule and which ones on the right; to automatically adjust of most relevant ranges for numerical attributes; to rapidly discover the reduced high-quality rules directly without generating the frequent itemsets; to ensure the rules to be comprehensible, surprising, interesting, accurate, confidential, and etc.; to adjust all of these processes without the need for the metrics to be pre-determined for each dataset. Recently, some researchers have considered quantitative association rule mining as a multi-objective problem that best meets different criteria at the same time. In this paper, the parameter analysis of non-dominated sorting genetic algorithm-II based QAR-CIP-NSGA-II, which aims to maximize comprehensibility, interestingness, and performance for quantitative association rule mining problem, has been performed. For this purpose, to the best of our knowledge the effects of the parameters of QAR-CIP-NSGA-II such as the number of evaluations, population number, mutation probability, amplitude and threshold value to the number of rules obtained, average support, confidence, lift, certainty factor, netconf, and the number of records covered in five real-world data whose attributes consist of quantitative values have been carried out for the first time in this study. Detailed sensitivity analysis results are presented and interpreted in comparative tables.

Keywords

quantitative association rules mining, multi-objective optimization, non-dominated sorting genetic algorithm-II

Nicel Birliktelik Kural Madenciliği İçin Baskın Olmayan Sıralama Genetik Algoritma-II’nin Duyarlılık Analizi

Abstract

İkili ya da kesikli değerlere sahip veri kümelerine odaklanan birçok birliktelik kural madenciliği çalışması vardır. Ancak, gerçek dünya uygulamalarındaki veriler genellikle nicel değerlerden oluşmaktadır. Nicel veriler için keşfedilecek kurallarda hangi niteliklerin olacağı ve hangilerinin kuralın solunda hangilerinin sağında olacağının belirlenmesi, ilgili nicel aralıkların en uygun şekilde otomatik ayarlanması; kuralların yoğun nesne kümeleri üretilmeden tek aşamada anlaşılabilir, doğru, güvenilir, ilginç, sürpriz vb. özelliklere sahip olacak şekilde bulunması ve tüm bu işlemlerin her veri tabanı için önceden belirlenmesi gereken metriklere ihtiyaç duyulmadan ayarlanması zor bir problemdir. Yakın zamanda bazı araştırmacılar, nicel birliktelik kural madenciliğini, farklı kriterleri aynı anda en iyi şekilde karşılayacak şekilde, çok amaçlı bir problem olarak düşünmüşlerdir. Bu makalede nicel birliktelik kural madenciliği problemi için anlaşılabilirlik, ilginçlik ve performansı en üst düzeye çıkarmayı amaçlayan çok amaçlı evrimsel algoritmalardan baskın olmayan sıralama genetik algoritma-II temelli QAR-CIP-NSGA-II’nin parametre analizi yapılmıştır. Bu amaçla; nitelikleri nicel değerler alan beş gerçek dünya verisinde QAR-CIP-NSGA-II’nin değerlendirme sayısı, popülasyon sayısı, mutasyon olasılığı, genlik ve eşik değeri gibi parametrelerinin; elde edilen kural sayısı, ortalama destek, güven, lift, kesinlik faktörü, netconf ve kapsanan kayıt sayısını nasıl değiştirdiği kapsamlı bir şekilde bildiğimiz kadarıyla ilk kez bu çalışmada gerçekleştirilmiştir. Detaylı analiz sonuçları karşılaştırmalı tablolar ile sunulmuştur ve yorumlanmıştır.

Keywords

nicel birliktelik kural madenciliği, çok amaçlı optimizasyon, baskın olmayan sıralama genetik algoritma-II

References

• K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, In International Conference on Parallel Problem Solving From Nature, Springer, Berlin, Heidelberg, 849-858, 2000.
• D. Martín, A. Rosete, J. Alcalá-Fdez, F. Herrera, “QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules”, Information Sciences, 258, 1-28, 2004.
• B. Oğuz Yolcular, U. Bilge, M. K. Samur, “Kulak burun boğaz taburcu notlarından birliktelik kurallarının çıkartılması”, Bilişim Teknolojileri Dergisi, 11(1), 35-42, 2018.
• S. Ramaswamy, S. Mahajan, A. Silberschatz, “On the discovery of interesting patterns in association rules”, In: 24rd International Conference on Very Large Data Bases, San Francisco, CA, USA, 1998.
• E. Shortliffe, B. Buchanan, “A model of inexact reasoning in medicine”, Mathematical Biosciences, 23(3–4), 351–379, 1975.
• K. I. Ahn, J. Y. Kim, “Efficient mining of frequent itemsets and a measure of interest for association rule mining”, Journal of Information & Knowledge Management, 3(3), 245–257, 2004.
• R. Srikant, R. Agrawal, “Mining quantitative association rules in large relational tables”, In: Proceedings of ACM SIGMOD, 1–12, 1996.
• H. P. Chiu, Y. T. Tang, K. L. Hsieh, “A cluster-based method for mining generalized fuzzy association rules”, In First International Conference on Innovative Computing, Information and Control, 2, 519-522, 2006.
• B. Alatas, E. Akin, A. Karci, “MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules”, Applied Soft Computing, 8, 646-656, 2008.
• C. H. Chen, T. P. Hong, V. S. Tseng, L. C. Chen, “A multi-objective genetic-fuzzy mining algorithm”, In IEEE International Conference on Granular Computing, IEEE, GrC 2008, 115-120, 2008.
• X. Yan, C. Zhang, S. Zhang S, “Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support”, Expert Systems with Applications, 36(2), 3066-3076, 2009.
• V.Beiranvand, M. M. Kashani, A. A. Bakar, “Multi-Objective PSO algorithm for mining numerical association rules without a priori discretization”, Expert Systems with Application, 41, 4259-4273, 2014.
• D. Martin, A. Rosete, A. J. Fdez, F. Herrera, “QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules”, Information Sciences, 258, 1-28, 2014.
• J. Piri , R. Dey, “Quantitative association rule mining using multi-objective particle swarm optimization”, International Journal of Scientific & Engineering Research, 5(10), 155-161, 2014.
• M. Almasi, M. S. Abadeh, “Rare-PEARs: A new multi objective evolutionary algorithm to mine rare and non-redundant quantitative association rules”, Knowledge-Based Systems, 89, 366-384, 2015.
• I. Kahvazadeh, M. S. Abadeh, “MOCANAR: A multi-objective cuckoo search algorithm for numeric association rule discovery”, Computer Science & Information Technology, 99-113, 2015.
• M. Martínez-Ballesteros, A. Troncoso, F. Martínez-Álvarez, J. C. Riquelme, “Improving a multi-objective evolutionary algorithm to discover quantitative association rules”, Knowledge and Information Systems, 49(2), 481-509, 2016.
• R. J. Kuo, M. Gosumolo, F. E. Zulvia, “Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining”, Neural Computing and Applications, 1-14, 2017.
• D. Yan, X. Zhao, R. Lin, D. Bai, “PPQAR: Parallel PSO for quantitative association rule mining”, In IEEE International Conference on Big Data and Smart Computing (BigComp), 163-169, 2018.
• I. E. Agbehadji, S. Fong, R. Millham, “Wolf search algorithm for numeric association rule mining”, In IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 146-151, 2016.
• B. Badhon, M. M. J. Kabir, S. Xu, M. Kabir, “A survey on association rule mining based on evolutionary algorithms”, International Journal of Computers and Applications, 1-11, 2019
• E. V. Altay, B. Alatas, “Performance analysis of multi-objective artificial intelligence optimization algorithms in numerical association rule mining”, Journal of Ambient Intelligence and Humanized Computing, 1-21, 2019.
• E. V. Altay, B. Alatas, “Intelligent optimization algorithms for the problem of mining numerical association rules”, Physica A: Statistical Mechanics and its Applications, 540, 123142, 2020.
• T. Zhang, M. Shi, J. Wang, G. Yang,” P-EAARM: A generic framework based on spark for eas-based association rule mining”, In IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), IEEE, 99-104, 2019.
• Internet: H. A. Guvenir, I. Uysal, Bilkent university function approximation repository, http://funapp.cs.bilkent.edu.tr/DataSets, 15.12.2018.