کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6837327 | 618422 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A comprehensive study on the effects of using data mining techniques to predict tie strength
ترجمه فارسی عنوان
یک مطالعه جامع در مورد اثرات استفاده از تکنیک های داده کاوی برای پیش بینی قدرت کراوات
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کلمات کلیدی
داده کاوی، قدرت اتصال مدل مبتنی بر رفتار رفتاری، تکنیک های طبقه بندی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
In this paper, the problem of tie strength prediction is modeled as a data mining problem on which different supervised and unsupervised mining methods are applicable. We propose a comprehensive study on the effects of using different classification techniques such as decision trees, Naive Bayes and so on; in addition to some ensemble classification methods such as Bagging and Boosting methods for predicting tie strength of users of a social network. LinkedIn social network is used as a real case study and our experimental results are proposed on its extracted data. Several models, based on basic techniques and ensemble methods are created and their efficiencies are compared based on F-Measure, accuracy, and average executing time. Our experimental results show that, our profile-behavioral based model has much better accuracy in comparison with profile-data based models techniques.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Human Behavior - Volume 60, July 2016, Pages 534-541
Journal: Computers in Human Behavior - Volume 60, July 2016, Pages 534-541
نویسندگان
Mohammad Karim Sohrabi, Soodeh Akbari,