یاگیری توابع عضویت برای کاوش قوانین انجمنی فازی با استفاده از اتوماتای یادگیر

نوع مقاله : مقاله پژوهشی فارسی

نویسندگان

1 دانشکده مهندسی کامپیوتر، واحد ارومیه،دانشگاه آزاد اسلامی، ارومیه، ایران.

2 گروه کامپیوتر، واحد خوی، دانشگاه آزاد اسلامی، خوی، ایران

3 دانشکده مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد ارومیه ، ارومیه، ایران.

چکیده

تراکنش ها در مجموعه داده‌های وب اغلب از داده های کمّی تشکیل شده‌‌، که نشان می‌دهد تئوری مجموعه‌های فازی می‌تواند برای نشان دادن چنین داده هایی استفاده شود. مدت زمان صفحات وب که توسط کاربران ملاقات می‌شود‌، یکی از انوع داده ذخیره شده درلاگ های وب است که می-تواند به عنوان یک عامل مهم برای تحلیل رفتار حرکتی کاربران استفاده شود. هرچند، در تمامی کارهای انجام شده برای کاوش قوانین انجمنی از داده های مورد استفاده از وب تعداد و پارامترهای توابع عضویت در نظر گرفته شده برای پارامتر زمان، در تمام صفحات وب ثابت فرض شده است. این در حالی است که تعداد و پارامترهای توابع عضویت مورد استفاده برای هر صفحه وب با سایر صفحات وب متفاوت است. بنابراین برای حل این چالش در این مقاله‌، یک روش بهینه سازی یادگیری تقویتی مبتنی بر اتوماتای یادگیر با نام LA-OMF برای استخراج خودکار هر دوی تعداد و پارامترهای توابع عضویت ذوزنقه‌ای برای استخراج قوانین انجمنی فازی از داده‌های وب ارائه شده است. همچنین برای افزایش سرعت همگرایی روش پیشنهادی و حذف توابع عضویت نامناسب هیوریستیک جدیدی ارائه شد. کارایی روش پیشنهادی مورد ارزیابی قرار گرفت و نتایج با نتایج به دست آمده با استفاده از روش کاوش فازی وب در یک مجموعه داده واقعی مقایسه شد. آزمایشات بر روی مجموعه داده با اندازه های مختلف تأیید کرد که روش پیشنهادی LA-OMFبا استخراج توابع عضویت بهینه میانگین کارایی تابع هدف و پشتیبان فازی را در مقایسه با توابع عضویت یکنواخت به ترتیب 39% و 61% افزایش داده است.

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