مقایسه فنون داده‌کاوی و منطق فازی به‌منظور شناسایی رفتار مشتریان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه حسابداری، دانشکده علوم اداری و اقتصادی، دانشگاه فردوسی مشهد، مشهد، ایران

2 کارشناس ارشد حسابداری، دانشکده حسابداری، دانشگاه آزاد اسلامی بیرجند، بیرجند، ایران

چکیده

این تحقیق به مقایسه فنون داده‌کاوی و منطق فازی در شناسایی رفتار مشتریان و شنیدن صدای آن‌ها به‌منظور استفاده در فرآیند هزینه‌یابی هدف می‌پردازد. داده‌های تحقیق مربوط به انبار داده‌های فروش شرکت کاشی فرزاد در سال‌های 93 و 94 است. نتایج حاصل از آزمون فرضیات تحقیق بیانگر این موضوع است که میزان پیش‌بینی صحیح ویژگی‌های مدنظر مشتریان در شبکه عصبی–فازی با تابع فعال‌ساز خوشه‌بندی فازی 941/0، در شبکه عصبی پرسپترون چندلایه با تابع فعال‌ساز سیگموئید 927/0، در شبکه عصبی پرسپترون چندلایه با تابع تانژانت 882/0 و در شبکه تابع پایه شعاعی با تابع سافت‌مکس 918/0 است. نتایج نشان می‌دهد که شبکه عصبی–فازی نسبت به سایر روش‌های مورداستفاده، نتیجه ویژگی‌های مدنظر مشتریان را بهتر می‌تواند پیش‌بینی کند.

کلیدواژه‌ها


عنوان مقاله [English]

Comparing data mining and fuzzy logic techniques to identify behavior of customers

نویسندگان [English]

  • Mahdi Salehi 1
  • Mehran Salari 2
1 Associate Professor, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
2 M.A holder in Accounting, Faculty of Accounting, Birjand Branch, Islamic Azad University, Birjand, Iran
چکیده [English]

This study compares data mining techniques and fuzzy logic for Identify customer behavior and voice of the customer to be used in the process of target costing.
In this research, the data relating to sales in the data warehouse of Farzad tile producing company in years 2014 and 2015 have been used. The results of the test of hypotheses suggest that the rate of correct prediction for customer features in neural-fuzzy networks with activation function, Fuzzy Clustering is 0.941and in Multi-layer Neural Network with sigmoid activation function is 0.927 and in the multi-layer neural network with tangent function is 0.882 and in Radial basis function network with Max softball function is 0.918. The results show that fuzzy neural network has better results than other methods used to predict the characteristics of the target customers.

کلیدواژه‌ها [English]

  • Target Costing
  • Data Mining
  • Multilayer Perceptron
  • Radial Basis Function
[1] Adeniyi, Segun, Idowu, "Impact of Target Costing on Competitive Advantage in the Manufacturing Industry: A Study of Selected Manufacturing Firms in Nigeria", International Journal of Academic Research in Accounting Finance and Management Science, Vol. 4, No.3, pp. 97–108, 2014.
[2] Suzan, Suleiman, Normah, Omar, Wee, Shu, Hui, Ibrahim Kamal, Abdul Rahman, Susumo, Ueno, Jimmy Tsay, Hussein, H. Hamood, "Integrating Target Costing (TC) Indicators within, the Balanced Scorecard (BSC) Model in Selected Asian A Research Framework", Islamic Accounting and Financial Criminology Conference , Vol. 8, No. 29, pp.1-13, 2013.
[3] Woods, Margaret, Lynda Taylor, Gloria, Cheng Ge Fang, "A case study of economic value added in target costing", Management Accounting Research, Vol. 23, pp. 261-277, 2012.
[4] Lawrence, Imeokparia, Sanusi, Adebisi, "Target Costing and Performance of Manufacturing Industry in South-Western Nigeria", Global Journal of Management and Business Research Accounting and Auditing, Vol. 14, Issue 4, pp. 50-58, 2014.
[5] Ansari, S, Bell, J. & Okona, H, "Target costing: the uncharted research territory", Hand Book of Management Accounting Research, Vol. 2, pp. 507-530, 2007.
[6] Rezaie Dolat abadi, Hosayn; Salehzade, Reza; Atarpor, Mohamad reza and Baluie jam khane, Hadi; Management costs through product design: A model combining the methods of target costing, QFD and value engineering, Production and Operations Management, Vol. 3, No2,pp77-88,2012.
[7] Tarokh, Mohamad jafar and Sharifiyan, Kobra, Application of data mining to improve customer relationship management, Industrial Management Studies, Vol. 6, No17, pp. 153-181,2010.
[8] Olabisi, Jyeola and Dafe, Paul, Onou, "Implementing Target Costing in Small and Medium Scale Enterprises in Ogun Industrial Metropolis", International Journal of Humanities and Social Science, Vol. 4, No8, pp.222-233, 2014.
[9] Peppared, J, Customer Relationship management in financial services, European management Journal, Vol,18, No.3,  pp. 312-327, 2000.
[10] Dastgir, Mohsen and Shafiei Sardasht, Morteza, data mining technology: New Approach in the field of finance, accounting knowledge, year11, No5, pp. 6-27,2011.
 [11] Chen, YH & Su, CT, ''A keno model for customer knowledge discovery'', Total Quality Management & Business Excellence, Vol. 17, No.5, pp.589-608, 2006.
[12] Chan, C & Lewis, B, 'A basic primer on data mining', Information Systems ,Management, Vol. 19, No. 4, pp. 56-60, 2002.
[13] Hun, j. And Kamber, p, "Data Mining: Concepts and Techniques", Dartmouth Publishing Inc, No 15, pp. 4-15, 2001.
[15] Rasoulian, Mohsen, Sharayee, Abolghasem, and Gvhrdany, Mohamed, The role of data mining in association rules in Strategic Management, Basirat Journal, Vol. 15, No. 39, pp. 74 -100, 2008.
 [15] Lu, C, And Ta-Cheng Chen, "A Study of Appling data mining approach to the information disclosure for Taiwan stock market investors", Expert Systems whit Application, Vol. 36,  pp,3536-3542, 2009.
[16] Moradi, Golmorad and Ghasemi, Vahid, data mining technique and its application in Social Studies, Journal of Social Sciences Faculty of Literature and Humanities University of Mashhad, Vol. 9, pp. 157-178,2010.
 [17] Theodoridis, P.K And Chatzipan agiotou, K.C, 'Store image attributes and customer satisfaction on across different customer profiles within the supermarket sector in Greece ', European Journal of Marketing 43 , pp 708-734, 2009.
[18] M. Garcia Murillo, H. Annabi , Customer knowledge management, Journal of the
Operational Research Society 53, pp. 875–884, 2002.
[19] R. Uma, Maheswari, S Saravana, Mahesan, Tamilarasan, A, K, Subramani, "Role of Data Mining in CRM", International Journal of Engineering Research, Volume No.3, Issue No.2, pp.75-78, 2014.
[20] Sepehri, Mohammad Mahdi; Norouzi, Ashraf; Teymourpour, Babak and Choubdar, Sarvenaz, Discovering the reasons for turning away a customer of banking services by combining data mining methods and survey, Management Researches in Iran, Vol 4, pp. 27-125, 2012.
[21] E.W.T. Ngai, Li Xiu, D.C.K. Chau, ''Application of data mining techniques in customer relationship management'' :A literature review and classification, Expert Systems with Applications, Vol. 36, pp. 2592-2602, 2009.
[22] Bashiri mousavi, Seyed Alireza; Afsar, Amir and Mahjoubi Fard, Arash, Bank customers value analysis using data mining techniques and fuzzy hierarchical analysis, Management Researches in Iran, Vol 1, pp. 23-43, 2015.
[23] Sinai, Hasan ali; Mortazavi, Saeed Allahi and Timuriasl, Yasser, Tehran Stock Exchange index forecast Using artificial neural networks, evaluation of accounting and auditing, Vol. 12, No. 41, pp. 59-83.2005.