Veri Madenciliği ve Makine Öğrenimi Yaklaşımlarının Karşılaştırılması: Tekstil Sektöründe bir Uygulama

Abstract

Her gün gelişmekte ve büyümekte olan teknoloji, modern dünyanın vazgeçilmez bir unsuru olmuştur. Teknolojinin hızla gelişmesiyle bilgisayar kullanımı artan dünyamızda daha fazla veri depolanmaya başlanmıştır. Oluşan bu büyük veriler tek başlarına bir anlam ifade etmemektedir. Ancak veri ve analitik alanda yetkinliklerin artırılması ile belirli örüntülere dayalı çıkarımlardan anlamlılık boyutu kazanırlar. Örüntülerin belirlenebilmesini sağlayan, yapılacak araştırmaya ve veri tipine uygun veri madenciliği ve makine öğrenimi teknikleri bulunmaktadır. Bu teknikleri ile veriler arasındaki kural, kalıp ve ilişkiler bulunur. Veri madenciliği ve makine öğrenimi teknikleri birçok farklı sektörde farklı amaçlarla kullanılabilmektedir. Bu çalışmada veri madenciliği ve makine öğrenimi arasındaki benzerlik ve farklılıklar ortaya konmaya çalışılmış ve bu disiplinlerin; veri bilimi, istatistik ve diğer disiplinler ile ortak ve ayrıştığı noktalar tespit edilmeye çalışılmıştır. Ayrıca çalışmada pantolon üreten bir tekstil firmasının verileri kullanılarak, R Studio, Python ve Knime makine öğrenimi programları yardımıyla, çoklu doğrusal regresyon, yapay sinir ağları ve karar ağaçları teknikleri uygulanmış, tahmini model sonuçlar bulunmuş ve model performansları karşılaştırılmıştır. Çalışmanın sonucunda tahminleme başarısında en iyi algoritmanın yapay sinir ağları ve en iyi makine öğrenimi programının RStudio programı olduğu sonucuna varılmıştır.

Keywords

• Tekstil Sektörü
• Makine Öğrenimi
• Çoklu Doğrusal Regresyon
• Yapay Sinir Ağları
• Karar Ağaçları
• Python
• RStudio
• Knime

Comparison of Data Mining and Machine Learning Approaches: An Application in Textile Industry

Abstract

Technology, which is developing and growing every day, has become an indispensable whole of the modern world. With the rapid development of technology, more data has begun to be stored in our world, where the use of computers is increasing. These big data do not mean anything on their own. However, they gain a meaningful dimension from inferences based on certain patterns by increasing their competencies in data and analytics. There are data mining and machine learning techniques suitable for the research and data type to be made, enabling the determination of patterns. With these techniques, there are rules, namely algorithms, between the data. Data mining and machine learning techniques can be used for different purposes in many different sectors. In this study, the similarities and differences between data mining and machine learning have been tried to be revealed and these disciplines; It has been tried to determine the common and divergent points with data science, statistics and other disciplines. In addition, using the data of a textile company producing trousers, multiple linear regression, artificial neural networks and decision trees techniques were applied with the help of R Studio, Python and Knime machine learning programs, and estimated model results were found and model performances were compared. As a result of the study, it was concluded that the best algorithm in predicting success is artificial neural networks and the best machine learning program is RStudio.

Keywords

• Textile Sector
• Machine learning
• Multiple Linear Regression
• Neural Networks
• Decision Trees
• Python
• RStudio
• Knime

References

1. Accentura (2021). Artificial intelligince. Erişim: 12 Eylül. 2021. https://www.accenture.com/in-en/insights/artificial-intelligence-summary-index.
2. AI, D. (n.d.). Association Learning. Deep AI. https://deepai.org/machine-learning-glossary-and-terms/association-learning
3. Algorithmia. (2020). 2020 State of Enterprise Machine Learning. https://info.algorithmia.com/hubfs/2019/Whitepapers/The-State-of-Enterprise-ML-2020/Algorithmia_2020_State_of_Enterprise_ML.https://algorithmia.com/state-of-ml.
4. Analytics Insigth (2021). Top Machine learning tools used by experts in 2021. https://www.analyticsinsight.net/top-machine-learning-tools-used-by-experts-in-2021. Erişim 12 Ekim, 2021.
5. Angrist, J. D., & Pischke, J. S. (2008). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
6. Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: a case study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291–300.
7. Analytic Insight (2021). Top 10 data mining algorithms 2021. Erişim: 21 Temmuz 2021. https://www.analyticsinsight.net/top-10-data-mining-algorithms-2021/
8. Bergstra, J., Ca, J. B., & Ca, Y. B. (2012). Random search for hyper-parameter optimization Yoshua Bengio. Journal of Machine Learning Research (Vol. 13). http://scikit-learn.sourceforge.net.

9. Birkhold, C., Tamagnini, P., & Schmid, S. (2019). How to automate machine learning | KNIME. https://www.knime.com/blog/how-to-automate-machine-learning.
10. Breck, E.; Cai, S.; Nielsen, E.; Salib, M.; Sculley, D. The ML test score: A rubric for ML production readiness and technical debt reduction (2017). In Proceedings of the 2017 IEEE International Conference on Big Data (Big Data). Boston. MA, USA, 11–14 December. pp. 1123–1132.
11. Brownlee, J. (2020). 6 dimensionality reduction algorithms with python. https://machinelearningmastery.com/dimensionality-reduction-algorithms-with-python/
12. Buffet, O., Pietquin, O., & Weng, P. (2020). Reinforcement learning. In arXiv (Vol. 3, issue 3, p. 1448). arXiv. https://doi.org/10.4249/scholarpedia.1448.
13. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T.P., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide.
14. Chollet, F. (2017). Deep learning with python. Manning Publications.
15. Dataversity website (2021). A brief history of machine learning. Erişim: 05 Eylül. 2021. https://www.dataversity.net/a-brief-history-of-machine-learning/
16. Educba website (2021). Data mining vs machine learning. Erişim: 21 Eylül 2021. https://www.educba.com/data-mining-vs-machine-learning/
17. Eisler, S., & Meyer, J. (2020). Visual analytics and human ınvolvement in machine learning. ArXiv, abs/2005.06057.
18. Ersöz, F. (2019). SPSS ile istatiksel veri analizi. Seçkin Yayıncılık.Ankara
19. Ersöz, F., & Ersöz, T. (2019). Veri madenciliği teknikleri ve uygulamaları. Seçkin Yayıncılık.Ankara
20. Fayyad, U.M., Piatetsky-Shapiro, G., and Smyth, P. (1996). Knowledge discovery and data mining: towards a unifying framework. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, 82–88.
21. Fazakis, N., Karlos, S., Kotsiantis, S., & Sgarbas, K. (2016). Self-Trained LMT for semisupervised learning. Computational Intelligence and Neuroscience, 2016, 3057481. https://doi.org/10.1155/2016/3057481.
22. Forbes (2018). 5 Entrepreneurs on the rise in AI. Erişim: 12 Eylül. https://www.forbes.com/sites/insights-intelai/2018/11/29/5-entrepreneurs-on-the-rise-in-ai/?sh=7c79e67cf99f
23. Fox, J., & Andersen, R. (2005). Using the R statistical computing environment to teach social statistics Cources. http://cran.r-project.org/.
24. Ersoz, F., Guler, E., Ersoz, T. (2017). Knowledge discovery and data mining techniques in textile industry. International Journal of Computer and Information Engineering. Vol. 11, No 7. 923-927.
25. Guo Yufeng. The 7 steps of machine learning. 2017. In: towardsdatascience.com
26. Gürsakal, N. (2018). Makine Öğrenmesi. Dora yayınları.
27. IBM Software (2021). Machine learning. Erişim: 28 Temmuz 2021. IBM Software Website: https://www.ibm.com/tr-tr/cloud/learn/machine-learning
28. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
29. Javapoint website (2021). Erişim: 16 Eylül 2021. https://www.javatpoint.com/data-mining-vs-machine-learning
30. KDnuggets website (2018). The 7 steps of machine learning. Erişim: 04 Eylül 2021. https://www.kdnuggets.com/2018/05/general-approaches-machine-learning-process.html
31. KDnuggets website (2020). History of data mining. Erişim: 20 Ekim 2021.https://www.kdnuggets.com/2016/06/rayli-history-data-mining.html
32. KDnuggets website (2021). 10 best data mining tools. Erişim: 04 Eylül 2021. https://www.kdnuggets.com/2021/01/machine-learning-algorithms-2021.html.
33. Knowlab website (2021). Erişim: 10 Ekim 2021. from https://knowlab.in/machine-learning-vs-data-mining-whats-the-difference/ Kubat, M., Bratko, I., & Michalski, R. (1996). A Review of Machine Learning Methods.
34. Kuhlman, D. (2009). A Python Book: Beginning Python, Advanced Python, and Python Exercises. http://www.davekuhlman.org
35. Kulin, Merima & Kazaz, Tarik & De Poorter, Eli & Moerman, Ingrid. (2021). A survey on machine learning-based performance ımprovement of wireless networks: PHY, MAC and Network Layer. Electronics. 10. 318. 10.3390/electronics10030318.
36. Legendre, A.M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot. Paris, 1805. “Sur la Méthode des moindres quarrés” appears as an appendix.
37. Lin, J.-Y., Lee, C.-Y., & Chang, R.-I. (2018). Improve quality and efficiency of textile process using data-driven machine learning in industry 4.0. International Journal of Technology and Engineering Studies, 4(2). https://doi.org/10.20469/ijtes.4.10004-2.
38. Lorente-Leyva, L. L., Alemany, M. M. E., Peluffo-Ordóñez, D. H., & Araujo, R. A. (2021). Demand forecasting for textile products using statistical analysis and machine learning algorithms.
39. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12672 LNAI, 181–194. https://doi.org/10.1007/978-3-030-73280-6_15
40. Lovell MC. Data mining. Rev Econ Stat 1983, 65:1– 11.
41. Lukman, I., & Natalina. (2019). Association rules and regression linear model of the groundwater population by the evaluation of uranium. MATEC Web of Conferences, 270, 04017. https://doi.org/10.1051/matecconf/201927004017.
42. McCorduck, Pamela (2004), Düşünen Makineler (2. baskı). Natick, MA: AK Peters Ltd.. ISBN 978-1-56881-205-2, OCLC 52197627.
43. Malaca, P., Luis, ·, Rocha, F., Gomes, · D, Silva, J., Germano Veiga, ·, & Luis, B. (2019). Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry. J Intell Manuf, 30, 351–361. https://doi.org/10.1007/s10845-016-1254-6.
44. Mariscal, G., Marbán, Ó., & Fernández, C. (2010). A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review, 25, 137–166.
45. Martínez-Plumed F, Contreras-Ochando L, Ferri C, Orallo JH, Kull M, Lachiche N, Ramírez-Quintana MJ, Flach PA (2019) CRISP-DM twenty years later: from data mining processes to data science trajectories. IEEE Trans Knowl Data Eng 33(8):3048–3061.
46. May, S. (2019). 6 Reasons to learn data science with python | benefits of python data science training. https://www.zeolearn.com/magazine/benefits-of-learning-data-science-with-python.
47. Mayo, M. (2018). Frameworks for Approaching the Machine Learning Process-KDnuggets. Erişim: 12 Eylül 2021. https://www.kdnuggets.com/2018/05/general-approaches-machine-learning-process.html
48. Mayo, Matthew. The 7 Steps of Machine Learning, In: KDnuggets.com, 2018
49. Mitchell Guthrie, P. (2014). Looking backwards, looking forwards: SAS, data mining, and machine learning. https://blogs.sas.com/content/subconsciousmusings/2014/08/22/looking-backwards-looking-forwards-sas-data-mining-and-machine-learning/#prettyPhoto/0/)
50. Mitchell, T.; Buchanan, B.; DeJong, G.; Dietterich, T.; Rosenbloom, P.; Waibel, A. Machine learning. Annu. Rev. Comput. Sci. 1990, 4, 417–433.
51. Mozafary, V., & Payvandy, P. (2014). Application of data mining technique in predicting worsted spun yarn quality. Journal of the Textile Institute, 105(1), 100–108. https://doi.org/10.1080/00405000.2013.812552
52. Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America. 116(44), 22071–22080. https://doi.org/10.1073/pnas.1900654116.
53. Özbek, A., Akalın, M. (2011). The prediction of Turkey’s denim trousers export to Germany with ANN models. Tekstil ve Konfeksiyon. 21(4):313-322. İstanbul.
54. Patel, K., Fogarty, J., Landay, J., and Harrison, B. (2008). Investigating statistical machine learning as a tool for software development. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08). Association for Computing Machinery, New York, NY, USA, 667–676.
55. Piatestsky, G. (2019). Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - KDnuggets. https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html/2
56. Portugal, I., Alencar, P., & Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. In Expert Systems with Applications (Vol. 97, pp. 205–227). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2017.12.020
57. Ryu, S., Lee, H., Lee, D. K., & Park, K. (2018). Use of a machine learning algorithm to predict individuals with suicide ideation in the general population. Psychiatry Investigation, 15(11), 1030–1036. https://doi.org/10.30773/pi.2018.08.27
58. SAS software (2021). Retrieved on september 14, 2021 from IBM Software Website: https://www.sas.com /en_us/insights/analytics/machine-learning.html
59. Seagate Technology. (2020). SEAGATE. Seagate. https://www.seagate.com/tr/tr/our-story/data-age-2025/
60. Selvanayaki, M., Vijaya, M. S., Jamuna, K. S., & Karpagavalli, S. (2010). Supervised learning approach for predicting the quality of cotton using WEKA. Communications in Computer and Information Science, 70, 382–384. https://doi.org/10.1007/978-3-642-12214-9_61
61. Shafiq, Muhammad & Tian, Zhihong & Bashir, Ali & Jolfaei, Alireza. (2020). Data mining and machine learning methods for sustainable smart cities traffic classification: A Survey. Sustainable Cities and Society. 60. 10.1016/j.scs.2020.102177.
62. Sherarer, C. (2000). The CRISP-DM model:the new blueprint for data mining. Journal of Data Warehousing, 5(4), 1–15.
63. Softwaretestinghelp website (2021). Data mining vs machine learning vs artificial intelligence vs deep learning. Erişim: 04 Ağustos 2021. https://www.softwaretestinghelp.com/data-mining-vs-machine-learning-vs-ai/
64. Sotirios P. Chatzis, Vassilis Siakoulis, Anastasios Petropoulos, Evangelos Stavroulakis, Nikos Vlachogiannakis (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications. Vol. 112.353-371.
65. Studer, Stefan & Bui, Binh & Drescher, Christian & Hanuschkin, Alexander & Winkler, Ludwig & Peters, Steven & Müller, Klaus-Robert. (2021). Towards CRISP-ML(Q): A machine learning process model with quality assurance methodology. machine learning and knowledge extraction. 3. 392-413. 10.3390/make3020020.
66. Su, J., & Zhang, H. (2006). A fast decision tree learning algorithm introduction and related work. www.aaai.org
67. Sumathi, S., Sivanandam S.N., “Data mining tasks, techniques and applications, studies in computational ıntelligence (SCI)”, Springer-Verlag, Berlin.189-216.
68. Szepesv´ari, C. (2009). Algorithms for Reinforcement Learning. Morgan & Claypool Publishers.
69. Taranto-Vera, G., P. Galindo-Villardón, J. Merchán-Sánchez-Jara, J. Salazar-Pozo, A. Moreno-Salazar and V. Salazar-Villalva, 2021. Algorithms and software for data mining and machine learning: A critical comparative view from a systematic review of the literature. J. Supercomputing. Vol. 2021.
70. Tanzeel U. Rehman, Md. Sultan Mahmud, Young K. Chang, Jian Jin, Jaemyung Shin (2019). Current and future applications of statistical machine learning algorithms for agricultural machine vision systems, Computers and Electronics in Agriculture. Volume 156. Pages 585-605.
71. IOBE. (2021). TIOBE - The software quality company. https://www.tiobe.com/tiobe-index/Tiwari, A., & Sekhar, A. K. T. (2007). Workflow based framework for life science informatics. In Computational Biology and Chemistry (Vol. 31, Issues 5–6, pp. 305–319). https://doi.org/10.1016/j.compbiolchem.2007.08.009
72. Wang, S.-C. (2003). Artificial Neural Network. Interdisciplinary Computing in Java Programming. 81–100. https://doi.org/10.1007/978-1-4615-0377-4_5
73. Wen, H., & Gu, Q. (2014). The elements of supply chain management in new environmental era. Lecture Notes in Electrical Engineering. 242 LNEE(VOL. 2), 867–880. https://doi.org/10.1007/978-3-642-40081-0_74
74. Yufeng, G. (2017). The 7 Steps of Machine Learning (pp. 1–13). https://livecodestream.dev/post/7-steps-of-machine-learning/