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

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