ارائه روشی برای کاهش حساسیت الگوریتم های خوشه‌بندی افزایشی اسناد XML مبتنی بر الگوریتم های هوش دسته جمعی

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مدیریت، دانشکده مدیریت، دانشگاه علوم و تحقیقات، تهران، ایران

چکیده

تاکنون روشهای مختلفی برای ذخیره سازی و بازیابی اطلاعات اسناد نیمه ساخت یافته ارائه شده است که بیشتر آنها در دو گروه با رهیافت دسته ای و افزایشی قرار می گیرند. در رهیافت دسته ای یا خوشه ای فرض بر این است که کل اسناد قابل دسترسی و خوشه بندی است و اسناد می توانند چندین بار مورد پردازش قرار گیرند که باعث افزایش زمان اجرای اینگونه الگوریتم ها می شود. در رهیافت افزایشی کل اسناد تماماً یک جا وجود ندارند بلکه به مرور زمان در اختیار روش دسته بندی قرار می گیرد که از این نظر زمان اجرای اینگونه الگوریتم ها نسبت به روش دسته ای کمتر و در نتیجه سرعت اجرای آنها بیشتر است. در این پژوهش روش پیشنهادی ما با روش هایXCLS و XCLS+ در سه معیار ارزیابی Entropy، Purity و Fscore مورد مقایسه قرار گرفت. نتایج نشان داد روش پیشنهادی در معیارهای Entropy، Purity و Fscore نسبت به دو روش XCLS و XCLS+ ارجحیت دارد و فقط در معیار Fscore نسبت به روش XCLS+ اندکی کارایی کمتری از خود نشان می‌دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Presenting a method to reduce the sensitivity of incremental clustering algorithms of XML documents based on collective intelligence algorithms

نویسندگان [English]

  • Mohammad Nazari Farokhi
  • Ebrahim Nazari Farokhi
  • Ali Norouzbakhsh
Department of Management, Faculty of Management, University of Science and Research, Tehran, Iran
چکیده [English]

Until now, various methods have been presented for storing and retrieving information of semi-structured documents, most of them are placed in two groups with batch and incremental approach. In the batch or cluster approach, it is assumed that all the documents can be accessed and clustered, and the documents can be processed several times, which increases the execution time of such algorithms. In the incremental approach, all the documents do not exist in one place, but over time, they are provided to the classification method, and from this point of view, the execution time of such algorithms is less compared to the batch method, and as a result, their execution speed is faster. In this research, our proposed method was compared with XCLS and XCLS+ methods in three evaluation criteria: Entropy, Purity and Fscore. The results showed that the proposed method is preferable to the XCLS and XCLS+ methods in terms of Entropy, Purity and Fscore, and it is slightly less efficient than the XCLS+ method only in the Fscore criterion.

کلیدواژه‌ها [English]

  • particle optimization algorithm
  • semi-structured documents
  • incremental clustering
  • collective intelligence

مراجع

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