Temat: R-Project - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A Level-Based Approach to Prioritize Telecommunications R&D
Autorzy:: Fialho, J.
Godinho, P.
Costa, J. P.
Afonso, R.
Regalado, J. G.
Powiązania:: https://bibliotekanauki.pl/articles/307952.pdf
Data publikacji:: 2008
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: multicriteria decision support
R&D project evaluation
Opis:: In this paper, an approach to evaluate R&D projects in telecommunications is presented. These Project have particular features that cannot be properly incorporated by classical evaluation methods. This approach incorporates different criteria, both quantitative and qualitative, and also management flexibility and uncertainty. Thus, it is an approach that can be applied to real data of R&D projects in a telecommunications company.
Źródło:: Journal of Telecommunications and Information Technology; 2008, 4; 40-43
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario
Autorzy:: Suchacka, G.
Skolimowska-Kulig, M.
Potempa, A.
Powiązania:: https://bibliotekanauki.pl/articles/308645.pdf
Data publikacji:: 2015
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: data mining
e-commerce
k-Nearest Neighbors
k-NN
log file analysis
online store
R-project
supervised classification
web mining
Web store
Web traffic
Web usage mining
Opis:: This paper addresses the problem of classification of user sessions in an online store into two classes: buying sessions (during which a purchase confirmation occurs) and browsing sessions. As interactions connected with a purchase confirmation are typically completed at the end of user sessions, some information describing active sessions may be observed and used to assess the probability of making a purchase. The authors formulate the problem of predicting buying sessions in a Web store as a supervised classification problem where there are two target classes, connected with the fact of finalizing a purchase transaction in session or not, and a feature vector containing some variables describing user sessions. The presented approach uses the k-Nearest Neighbors (k-NN) classification. Based on historical data obtained from online bookstore log files a k-NN classifier was built and its efficiency was verified for different neighborhood sizes. A 11-NN classifier was the most effective both in terms of buying session predictions and overall predictions, achieving sensitivity of 87.5% and accuracy of 99.85%.
Źródło:: Journal of Telecommunications and Information Technology; 2015, 3; 64-69
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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