9783659772900 - Web Usage Mining of Hidden Knowledge Using Server Log Files de Abdul Ghani, Wajih (7 resultados)

Paperback. Condición: Brand New. 152 pages. 8.66x5.91x0.35 inches. In Stock.

Taschenbuch. Condición: Neu. Web Usage Mining of Hidden Knowledge Using Server Log Files | Wajih Abdul Ghani | Taschenbuch | 152 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783659772900 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Due to the hugeness of information available on the World Wide Web (WWW), extracting novel and useful knowledge from the web has gained significant attention among researchers in web mining. This type of mining has been used in three particular ways, web content mining, web structure mining, web usage mining. This paper is related to web usage mining by using the association rules and suggested algorithms to extract hidden knowledge in the log file. Extracting these types of knowledge required many of KDD steps such as preprocessing, pattern discovery, and pattern analysis. After that, the developed Apriori algorithm is adopted to mine the association rules from frequent pages or frequent IPs. The approach discussed in this paper, helps the system administrator and web designers to improve their web site design and helps to improve their website usability and visitor's browsing experience by determining related link connections in the website. 152 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abdul Ghani WajihMy name is Wajih Abdul Ghani Abdul Hussain, from Baghdad in Iraq, I studied in university of Baghdad, college of science, department of computer. I graduated in 2006. Since that year, I developed my skills in program.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Due to the hugeness of information available on the World Wide Web (WWW), extracting novel and useful knowledge from the web has gained significant attention among researchers in web mining. This type of mining has been used in three particular ways, web content mining, web structure mining, web usage mining. This paper is related to web usage mining by using the association rules and suggested algorithms to extract hidden knowledge in the log file. Extracting these types of knowledge required many of KDD steps such as preprocessing, pattern discovery, and pattern analysis. After that, the developed Apriori algorithm is adopted to mine the association rules from frequent pages or frequent IPs. The approach discussed in this paper, helps the system administrator and web designers to improve their web site design and helps to improve their website usability and visitor's browsing experience by determining related link connections in the website.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Due to the hugeness of information available on the World Wide Web (WWW), extracting novel and useful knowledge from the web has gained significant attention among researchers in web mining. This type of mining has been used in three particular ways, web content mining, web structure mining, web usage mining. This paper is related to web usage mining by using the association rules and suggested algorithms to extract hidden knowledge in the log file. Extracting these types of knowledge required many of KDD steps such as preprocessing, pattern discovery, and pattern analysis. After that, the developed Apriori algorithm is adopted to mine the association rules from frequent pages or frequent IPs. The approach discussed in this paper, helps the system administrator and web designers to improve their web site design and helps to improve their website usability and visitor's browsing experience by determining related link connections in the website.