به جمع مشترکان مگیران بپیوندید!

تنها با پرداخت 70 هزارتومان حق اشتراک سالانه به متن مقالات دسترسی داشته باشید و 100 مقاله را بدون هزینه دیگری دریافت کنید.

برای پرداخت حق اشتراک اگر عضو هستید وارد شوید در غیر این صورت حساب کاربری جدید ایجاد کنید

عضویت
جستجوی مقالات مرتبط با کلیدواژه

clustering method

در نشریات گروه برق
تکرار جستجوی کلیدواژه clustering method در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه clustering method در مقالات مجلات علمی
  • Azam Amin, Mohsen Jahanshahi *, Mohammadreza Meybodi
    In Software Defined Network (SDN), controller plane is separated from the data plane simplifying management. In these networks, data forwarding cannot be conducted just one controller. Therefore, it is needed to use multiple controllers in control plane. Since, switch-controller propagation delays and inter-controller latencies affect the performance, the problem of determining appropriate number of controllers as well as their suitable locations are two main challenges, which are known as NP-Hard. In this paper, a new clustering method based on K-means, K-Harmonics means and firefly algorithm named CPP-KKF is proposed for controller placement in SDN. Result obtained by CPP- KKF algorithm is benefitted by the advantages of all techniques. The proposed algorithm is evaluated on four topologies of TopologyZoo with different scales, that include Aarnet, Colt, Cognet, and DFN and the conducted simulations demonstrate that the proposed solution outperforms K-means, K-means++, Firefly and GSO algorithms in terms of aforementioned performance issues.
    Keywords: Software Defined Network, Controller Placement Problem, K-harmonics Mean, K-means, Firefly Algorithm, Clustering Method
  • Samira Babalou, Mohammad Javad Kargar, Seyyed Hashem Davarpanah
    Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, compared to OAEI (Ontology Alignment Evaluation Initiative), SeeCC has acceptable result with the top ten systems.
    Keywords: Ontology matching, Clustering method, Large, scale matching, Semantic graph
نکته
  • نتایج بر اساس تاریخ انتشار مرتب شده‌اند.
  • کلیدواژه مورد نظر شما تنها در فیلد کلیدواژگان مقالات جستجو شده‌است. به منظور حذف نتایج غیر مرتبط، جستجو تنها در مقالات مجلاتی انجام شده که با مجله ماخذ هم موضوع هستند.
  • در صورتی که می‌خواهید جستجو را در همه موضوعات و با شرایط دیگر تکرار کنید به صفحه جستجوی پیشرفته مجلات مراجعه کنید.
درخواست پشتیبانی - گزارش اشکال