Using Association Rules and Markov Model for Predit Next Access on Web Usage Mining

2007 ◽  
pp. 371-376 ◽  
Author(s):  
Siriporn Chimphlee ◽  
Naomie Salim ◽  
Mohd Salihin Bin Ngadiman ◽  
Witcha Chimphlee
Keyword(s):  
Markov Model ◽  
Association Rules ◽  
Web Usage Mining ◽  
Web Usage

scholarly journals Web usage mining with evolutionary extraction of temporal fuzzy association rules

2013 ◽  
Vol 54 ◽  
pp. 66-72 ◽  
Author(s):  
Stephen G. Matthews ◽  
Mario A. Gongora ◽  
Adrian A. Hopgood ◽  
Samad Ahmadi
Keyword(s):  
Association Rules ◽  
Web Usage Mining ◽  
Fuzzy Association Rules ◽  
Web Usage

COMPARISON OF INTERESTINGNESS MEASURES FOR WEB USAGE MINING: AN EMPIRICAL STUDY

2007 ◽  
Vol 06 (01) ◽  
pp. 15-41 ◽  
Author(s):  
XIANGJI HUANG
Keyword(s):  
Association Rules ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Interestingness Measures ◽  
Web Based ◽  
Log Data ◽  
Web Usage ◽  
Different Types ◽  
Mining Association Rules ◽  
Types Of Information

A common problem in mining association rules or sequential patterns is that a large number of rules or patterns can be generated from a database, making it impossible for a human analyst to digest the results. Solutions to the problem include, among others, using interestingness measures to identify interesting rules or patterns and pruning rules that are considered redundant. Various interestingness measures have been proposed, but little work has been reported on the effectiveness of the measures on real-world applications. We present an application of Web usage mining to a large collection of Livelink log data. Livelink is a web-based product of Open Text Corporation, which provides automatic management and retrieval of different types of information objects over an intranet, an extranet or the Internet. We report our experience in preprocessing raw log data, mining association rules and sequential patterns from the log data, and identifying interesting rules and patterns by use of interestingness measures and some pruning methods. In particular, we evaluate a number of interestingness measures in terms of their effectiveness in finding interesting association rules and sequential patterns. Our results show that some measures are much more effective than others.

Association rules mining based approach for Web usage mining

2011 ◽  
Author(s):  
Mohamad Nagi ◽  
Abdallah ElSheikh ◽  
Iyad Sleiman ◽  
Peter Peng ◽  
Mohammad Rifaie ◽  
...  
Keyword(s):  
Association Rules ◽  
Web Usage Mining ◽  
Association Rules Mining ◽  
Web Usage

scholarly journals Ekstraksi Click Stream Data Web E-Commerce Menggunakan Web Usage Mining

2021 ◽  
Vol 7 (2) ◽  
pp. 65-72
Author(s):  
Kartina Diah Kusuma Wardani
Keyword(s):  
World Wide Web ◽  
Association Rules ◽  
World Wide ◽  
Web Usage Mining ◽  
Frequent Itemset ◽  
Sequence Mining ◽  
Stream Data ◽  
Log Data ◽  
Web Usage

E-Commerce berkembang pesat dalam world wide web hingga menghasilkan berbagai jenis data yang dapat dianalisa lebih lanjut untuk berbagai keperluan seperti personifikasi web, profiling customer, dan sebagainya. Salah satu jenis data yang dihasilkan e-Commerce adalah click stream data web yang merekam aktivitas visitor web dalam bentuk log data selama berinteraksi pada laman web. Penelitian ini mengekstraksi click stream data web e-commerce untuk mendapatkan pola interaksi konsumen terhadap halaman web selama mengunjungi web e-commerce. Berdasarkan jenis data yang diekstrak maka web usage mining digunakan untuk ekstraksi pola dari click stream data yang berbentuk log data. Teknik mining yang dianalisa terhadap log data e-commerce pada penelitian ini terdiri dari frequent itemset, asociation rules, dan frequence sequence mining. Frequent itemset menghasilkan halaman web yang paling sering diakses oleh visitor. Association rules menghasilkan pola kemungkinan halaman web yang akan diakses visitor jika visitor mengakses halaman-halamn tertentu. Frequence sequence mining mendapatkan pola urutan halaman web yang paling sering diakses oleh visitor web e-commerce saat berinteraksi pada laman web. Pola urutan halaman yang diakses visitor menunjukkan urutan kebiasaan visitor mengunjungi e-commerce. Sedangkan teknik mining yang diimplementasikan untuk menghasilkan pola akses visitor pada penelitian ini adalah Frequence sequence mining. Hasil ekstraksi dari penelitian ini menunjukkan ada enam halaman web yang paling sering diakses oleh konsumen dengan berbagai pola urutan aksesnya.

A Web Usage Mining Approach to User Navigation Pattern and Prediction in Web Log Data

2012 ◽  
Vol 3 (4) ◽  
pp. 92-94
Author(s):  
SUJATHA PADMAKUMAR ◽  
◽  
Dr.PUNITHAVALLI Dr.PUNITHAVALLI ◽  
Dr.RANJITH Dr.RANJITH
Keyword(s):  
Web Usage Mining ◽  
Log Data ◽  
Web Log ◽  
Web Usage ◽  
User Navigation
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