روش جدیدی برای رتبه‌بندی قواعد حاصل از داده‌کاوی با استفاده از تحلیل پوششی داده‌ها

محورهای موضوعی : مدیریت صنعتی

Hossein Azizi ¹

1 - Department of Applied Mathematics, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran

تاریخ دریافت : 1395/04/11 تاریخ پذیرش : 1395/08/19 تاریخ انتشار : 1395/09/20

کلید واژه: Data envelopment analysis, کارآیی‌های خوشبینانه و بدبینانه, Optimistic and pessimistic efficiencies, Data mining, تحلیل پوششی داده‌ها, داده‌کاوی, قاعده‌ی انجمنی, جالب بودن, DEA with Double Frontiers, Association Rule, Interestingness, DEA با مرز دوگانه,

چکیده مقاله :

تکنیک‌های داده‌کاوی، یعنی استخراج الگوها از پایگاه‌های داده‌ای بزرگ، در تجارت به صورت گسترده‌ای مورد استفاده قرار می‌گیرند. با استفاده از این تکنیک‌ها ممکن است قواعد زیادی حاصل شوند و فقط تعداد کمی از آنها به دلیل محدودیت بودجه و منابع برای پیاده‌سازی در نظر گرفته شوند. ارزیابی و رتبه‌بندی جالب بودن و مفید بودن قواعد انجمنی در داده‌کاوی اهمیت زیادی دارد. در مطالعات قبلی که در مورد شناسایی قواعد انجمنی جالب از نظر ذهنی انجام شده است، اکثر روش‌ها مستلزم وارد کردن دستی یا پرسیدن از کاربر برای افتراق صریح قواعد جالب از ناجالب بوده است. این روش‌ها نیازمند محاسبات بسیار زیادی هستند و حتی ممکن است به نتیجه‌گیری‌های ناسازگار منتهی شوند. برای غلبه بر این مشکلات، این مقاله پیشنهاد می‌کند که از رویکرد تحلیل پوششی داده‌ها (DEA) با مرز دوگانه برای انتخاب کارآترین قاعده‌ی انجمنی استفاده شود. در این رویکرد علاوه بر بهترین کارآیی نسبی هر قاعده‌ی انجمنی، بدترین کارآیی نسبی آن نیز در نظر گرفته می‌شود. در مقایسه با DEAی سنتی، رویکرد DEA با مرز دوگانه می‌تواند کارآترین قاعده‌ی انجمنی را به درستی و به آسانی شناسایی کند. به عنوان یک مزیت، رویکرد پیشنهادی از نظر محاسباتی کارآمدتر از کارهای قبلی در این زمینه است. با استفاده از مثالی از تحلیل سبد بازار، قابلیت کاربرد روش مبتنی بر DEAی ما برای اندازه‌گیری کارآیی قواعد انجمنی با معیارهای چندگانه نشان داده خواهد شد.

چکیده انگلیسی:

Data mining techniques, i.e. extraction of patterns from large databases, are extensively used in business. Many rules may be obtained by these techniques and only a few of them may be considered for implementation due to the limitation of budgets and resources. Evaluating and ranking attractiveness and usefulness of the association rules is of paramount importance in data mining. In the earlier studies carried out on identifying mentally interesting association rules, most methods required writing information or asking users for explicit differentiation of interesting rules from uninteresting ones. These methods involve detailed calculations and they may even lead to inconsistent conclusions. To solve these problems, this article proposes the application of the double frontiers Data Envelopment Analysis (DEA) Approach for selecting the most effective association rule. In this approach, in addition to the best relative efficiency of each association rule, its worst relative efficiency is considered. Comparing with the traditional DEA, double frontiers DEA Approach is capable of identifying the most efficient association rule correctly and easily. As an advantage, the proposed approach is more efficient than the earlier works in this concern, as far as calculations are concerned. Applicability of our DEA-based method for measuring the efficiency of association rules will be shown by multiple criteria using an example of market basket analysis.

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