Improving the Efficiency of Term Weighting in Set of Dynamic Documents

Autor:	Mehdi Jabalameli, Mohammad Ali Nematbakhsh, Ala Arman
Rok vydání:	2015
Předmět:	Computer science media_common.quotation_subject Information quality computer.software_genre Computer Science Applications Education Weighting Term (time) Information system Quality (business) Data mining Set (psychology) computer media_common
Zdroj:	International Journal of Modern Education and Computer Science. 7:42-47
ISSN:	2075-017X 2075-0161
DOI:	10.5815/ijmecs.2015.02.06
Popis:	In real information systems, there are few static documents. On the other hand, there are too many documents that their content change during the time that could be considered as signals to improve the quality of information retrieval. Unfortunately, considering all these changes could be time-consuming. In this paper, a method has been proposed that the time of analyzing these changes could be reduced significantly. The main idea of this method is choosing a special part of changes that do not make effective changes in the quality of information retrieval; but it could be possible to reduce the analyzing time. To evaluate the proposed method, three different datasets selected from Wikipedia. Different factors have been assessed in term weighting and the effect of the proposed method investigated on these factors. The results of empirical experiments showed that the proposed method could keep the quality of retrieved information in an acceptable rate and reduce the documents‘ analysis time as a result.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7065fb02a16ccf8450affacbb99b8f4a https://doi.org/10.5815/ijmecs.2015.02.06 Zobrazit plný text záznamu