Synopsis
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.
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Web Usage Mining of Hidden Knowledge Using Server Log Files
Edité par
LAP LAMBERT Academic Publishing Mai 2017, 2017
ISBN 10 : 3659772909
ISBN 13 : 9783659772900
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659772900
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Web Usage Mining of Hidden Knowledge Using Server Log Files
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ISBN 10 : 3659772909
ISBN 13 : 9783659772900
Neuf
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Etat : 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. N° de réf. du vendeur 151428656
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Web Usage Mining of Hidden Knowledge Using Server Log Files
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LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 3659772909
ISBN 13 : 9783659772900
Neuf
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Web Usage Mining of Hidden Knowledge Using Server Log Files
Edité par
LAP LAMBERT Academic Publishing Mai 2017, 2017
ISBN 10 : 3659772909
ISBN 13 : 9783659772900
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 152 pp. Englisch. N° de réf. du vendeur 9783659772900
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Web Usage Mining of Hidden Knowledge Using Server Log Files
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LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 3659772909
ISBN 13 : 9783659772900
Neuf
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659772900
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Web Usage Mining of Hidden Knowledge Using Server Log Files
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LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 3659772909
ISBN 13 : 9783659772900
Neuf
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Taschenbuch. Etat : 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: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. N° de réf. du vendeur 109255311
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Web Usage Mining of Hidden Knowledge Using Server Log Files
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LAP LAMBERT Academic Publishing, 2017
ISBN 10 : 3659772909
ISBN 13 : 9783659772900
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