Association Rules as a Decision Making Model in the Textile Industry

Istrat, V.; Lalić, N.

doi:10.5604/01.3001.0010.2302

Artykuł - szczegóły

Tytuł artykułu

Association Rules as a Decision Making Model in the Textile Industry

Autorzy

Istrat V. , Lalić N.

Treść / Zawartość

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Identyfikatory

DOI

10.5604/01.3001.0010.2302

Warianty tytułu

Analiza koszykowa jako model decyzji w przemyśle tekstylnym

Języki publikacji

Abstrakty

Sales process disfunctions in the textile industry are problems that cause loss of customers, incomplete market supply, etc. The objective of the research is to analyse transactions from the textile industry database in order to find patterns in buyers’ behavior and improve the model of decision-making. Association rules, one of the most noticeable data mining techniques, is used as methodology to learn rules and market patterns that occur in sales in the textile industry, which will enhance the decision-making process, by making it more effective and efficient. The Apriori algorithm was applied and open source software Orange was used. It has been shown using a real-life dataset containing 2000 transactions from the textile industry of the South East Europe region that the approach proposed is useful in discovering effective knowledge in data associated with sales. The study reports new interesting rules and the dependence of the following parameters: support, confidence, lift and leverage on making more customized offers in the textile industry.

Nieprawidłowości w procesie sprzedaży w przemyśle tekstylnym powodują m.in. utratę klientów. Celem badania była analiza transakcji z bazy danych przemysłu tekstylnego w celu znalezienia wzorców na zachowaniach nabywców i udoskonalenia modelu decyzji. Analiza koszykowa, jako jedna z najlepiej dostrzegalnych technik kopiowania danych, jest stosowana w metodologii uczenia się reguł i wzorców rynkowych, które występują w sprzedaży w przemyśle tekstylnym zwiększając efektywność procesu podejmowania decyzji. W pracy użyto algorytmu Apriori i oprogramowania Orange. W pracy wykazano, że przy użyciu zestawu danych rzeczywistych zawierającego 2000 transakcji z przemysłu tekstylnego w regionie Europy Południowo-Wschodniej, proponowane podejście jest przydatne w odkrywaniu skutecznej wiedzy w zakresie danych związanych ze sprzedażą.

Słowa kluczowe

business intelligence association rules decision making textile industry

inteligencja biznesowa analiza transakcji podejmowanie decyzji przemysł tekstylny

Wydawca

Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny

Czasopismo

Fibres & Textiles in Eastern Europe

Rocznik

2017

Tom

Vol. 25, no. 4 (124)

Strony

8--14

Opis fizyczny

Bibliogr. 49 poz., rys., tab.

Twórcy

autor

Istrat V.

visnja.istrat@gmail.com

University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia

autor

Lalić N.

University of East Sarajevo, Faculty of Pedagogy, Bijeljina, Bosnia and Herzegovina

Bibliografia

1. Kasthuri S, Meyyappan T. Detection of sensitive items in market basket database using association rule mining for privacy preserving. In Pattern Recognition, Informatics and Mobile Engineering (PRIME) 2013 International Conference on 2013 Feb 21, pp. 200-203. IEEE.
2. Yabing J. Research of an improved apriori algorithm in data mining association rules. International Journal of Computer and Communication Engineering 2013; 1, 2(1): 25.
3. Gupta S, Mamtora R. A Survey on Association Rule Mining in Market Basket Analysis. International Journal of Information and Computation Technology. ISSN. 2014:0974-2239.
4. Minaei-Bidgoli B, Barmaki R, Nasiri M. Mining numerical association rules via multiobjective genetic algorithms. Information Sciences 2013; Jun 1, 233: 15-24.
5. Drucker P. My view on management. Adižes, Novi Sad. 2003.
6. Yabing J. Research of an improved apriori algorithm in data mining association rules. International Journal of Computer and Communication Engineering 2013; Jan 1, 2(1):25.
7. Tseng VS, Shie BE, Wu CW, Philip SY. Efficient algorithms for mining high utilimaty itemsets from transactional databases. IEEE transactions on knowledge and data engineering. 2013 Aug;25(8): 1772-86.
8. Delibašić B, Vukićević M, Jovanović M, Kirchner K, Ruhland J, Suknović M. An architecture for component-based design of representative-based clustering algorithms. Data & Knowledge Engineering 2012; May 31;75: 78-98.
9. Saaty TL. An eigenvalue allocation model for prioritization and planning. Energy Management and Policy Center, University of Pennsylvania. 1972:28-31.
10. Chauhan A, Cerpa. N. A comparison for procurement models for B2B Electronic Commerce. In Proceedings the OPTIMA Conference, Curico, Chile. 2001.
11. Astudillo C, Bardeen M, Cerpa N. Editorial: Data mining in electronic commerce-support vs. confidence. Journal of Theoretical and Applied Electronic Commerce Research 2014; Jan;9(1):i-vii.
12. Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases. InAcm sigmod record 1993; Jun 1, 22, No. 2: pp. 207-216. ACM.
13. Davenport TH, Prusak L. Working knowledge: How organizations manage what they know. Harvard Business Press; 1998.
14. Orange data mining tool, http://orange.biolab.si/
15. Grabmeier JL, Lambe LA. Decision trees for binary classification variables grow equally with the Gini impurity measure and Pearson's chi-square test. International Journal of Business Intelligence and Data Mining 2007; Jan 1;2(2): 213-26.
16. Xu Y. Research of Association Rules Algorithm in Data Mining. International Journal of Database Theory and Application 2016; Jun 30;9(6): 119-30.
17. Jafarzadeh H, Torkashvand RR, Asgari C, Amiry A. Provide a new approach for mining fuzzy association rules using apriori algorithm. Indian Journal of Science and Technology 2015; Apr 1;8(8): 707.
18. Kotsiantis SB. Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review 2012; Apr 1;37(4): 331-44.
19. Chen F, Deng P, Wan J, Zhang D, Vasilakos AV , Rong X. Data mining for the internet of things: literature review and challenges. International Journal of Distributed Sensor Networks 2015; Jan 1;2015:12.
20. Krüger A, Merceron A, Wolf B. A data model to ease analysis and mining of educational data. InEducational Data Mining 2010; Jun 11.
21. Verhoef PC, Venkatesan R, McAlister L, Malthouse EC, Krafft M , Ganesan S. CRM in data-rich multichannel retailing environments: a review and future research directions. Journal of Interactive Marketing 2010; May 31, 24(2): 121-37.
22. Lee CK, Tse YK, Ho GT, Choy KL. Fuzzy association rule mining for fashion product development. Industrial Management & Data Systems 2015; Mar 9; 115(2): 383-99.
23. Verhoef PC, Van Doorn J, Dorotic M. Customer value management: an overview and research agenda. Marketing ZFP. 2007 Dec 14; 29(JRM 2):105-22.
24. Ding S, Shi Z, Chen K, Azar AT. Mathematical modeling and analysis of soft computing. Mathematical Problems in Engineering 2015; Mar 23;2015.
25. Alpar P, Winkelsträter S. Assessment of data quality in accounting data with association rules. Expert Systems with Applications 2014; Apr 30, 41(5): 2259-68.
26. Fogliatto FS, da Silveira GJ, Borenstein D. The mass customization decade: An updated review of the literature. International Journal of Production Economics 2012; Jul 31, 138(1):14-25.
27. Kuo RJ, Lin YJ. Supplier selection using analytic network process and data envelopment analysis. International Journal of Production Research 2012; Jun 1, 50(11): 2852-63.
28. Ingle MG, Suryavanshi NY. Association Rule Mining using Improved Apriori Algorithm. International Journal of Computer Applications 2015; Jan 1, 112(4).
29. Suknović M, Delibašić B. Business intelligence and systems for support of decision making, FOS, Belgrade. 2010.
30. Suknović M. Development of methodology of support to group decision making. PhD dissertation, FOS, Belgrade. 2001.
31. Kamsu-Foguem B, Rigal F , Mauget F. Mining association rules for the quality improvement of the production process. Expert systems with applications 2013; Mar 31, 40(4): 1034-45.
32. Bahnsen AC, Stojanovic A, Aouada D , Ottersten B. Cost sensitive credit card fraud detection using bayes minimum risk. InMachine Learning and Applications (ICMLA) 2013; 12th International Conference on 2013 Dec 4 (Vol. 1, pp. 333-338). IEEE.
33. Pulakkazhy S, Balan RV. Data mining in banking and its applications-a review. Journal of Computer Science 2013; Oct 1, 9(10): 1252.
34. Shim B, Choi K , Suh Y. CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns. Expert Systems with Applications 2012; Jul 31, 39(9): 7736-42.
35. Orriols-Puig A, Martínez-López FJ, Casillas J , Lee N. A soft-computing-based method for the automatic discovery of fuzzy rules in databases: Uses for academic research and management support in marketing. Journal of Business Research 2013 Sep 30, 66(9): 1332-7.
36. Sánchez D, Vila MA, Cerda L , Serrano JM. Association rules applied to credit card fraud detection. Expert Systems with Applications 2009; Mar 31;36(2): 3630-40.
37. Orriols-Puig A, Martínez-López FJ, Casillas J , Lee N. A soft-computing-based method for the automatic discovery of fuzzy rules in databases: Uses for academic research and management support in marketing. Journal of Business Research 2013; Sep 30, 66(9): 1332-7.
38. Lee D, Park SH , Moon S. Utility-based association rule mining: A marketing solution for cross-selling. Expert Systems with Applications 2013; Jun 1, 40(7): 2715-25.
39. Lee CK, Choy KL, Ho GT, Chin KS, Law KM , Tse YK. A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry. Expert Systems with Applications 2013; Jun 1, 40(7): 2435-46.
40. Jevšnik S, Kalaoğlu F, Eryuruk SH, Bizjak M , Stjepanovič Z. Evaluation of a Garment Fit Model Using AHP. Fibres and Textiles in Eastern Europe 2015; 23, 2(110): 116-122.
41. Wu JZ, Liu HW , Wu FL. A recommender system based on car pairwise comparisons on a mobile application using association rules. In2016 IEEE International Conference on Industrial Technology (ICIT) 2016 Mar 14 (pp. 1344-1346). IEEE.
42. Agrawal R, Srikant R. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB 1994; September, 1215: pp. 487-499.
43. Cebeci U. Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert Systems with Applications 2009; Jul 31, 36(5): 8900-9.
44. Carvalho S, Portela F, Santos MF, Abelha A , Machado J. Step towards of a Homemade Business Intelligence Solution–A Case Study in Textile Industry. InNew Contributions in Information Systems and Technologies 2015 (pp. 361-370). Springer International Publishing.
45. Brito PQ, Soares C, Almeida S, Monte A , Byvoet M. Customer segmentation in a large database of an online customized fashion business. Robotics and ComputerIntegrated Manufacturing 2015; Dec 31, 36: 93-100.
46. Raichurkar P, Ramachandran M. Recent Trends and Developments in Textile Industry in India. International Journal on Textile Engineering and Processes 2015; Oct, 1(4).
47. Dadakas D, Katranidis SD. Perspectives for the textiles and clothing industry in Greece: Past experience, outlook and policy implications. SPOUDAI-Journal of Economics and Business 2013; Jan 25, 61(1-2): 13-38.
48. Pereira J, Basto M , Ferreira-da-Silva A. Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction. Journal of Management and Sustainability 2014; Feb 24, 4(1): 114.
49. Skiada M, Lekakos G, Gkika S , Bardaki C. Garment Recommendations for Online and Offline Consumers

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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