A Survey Report On Current Research and Development of Data Processing In Web Usage Data Mining

2016 ◽  
Vol 9 (5) ◽  
pp. 101-110 ◽  
Author(s):  
Nandita Agrawal ◽  
Anand Jawdekar
Keyword(s):  
Data Mining ◽  
Research And Development ◽  
Data Processing ◽  
Web Usage ◽  
Survey Report ◽  
Usage Data

Extraction of Target User Group from Web Usage Data Using Evolutionary Biclustering Approach

2012 ◽  
pp. 253-263
Author(s):  
R. Rathipriya ◽  
K. Thangavel ◽  
J. Bagyamani
Keyword(s):  
Data Mining ◽  
Greedy Heuristic ◽  
Data Mining Technique ◽  
Web Usage ◽  
Target User ◽  
Global Optimizer ◽  
User Groups ◽  
Optimal Target ◽  
Global Optimal ◽  
Usage Data

Data mining extracts hidden information from a database that the user did not know existed. Biclustering is one of the data mining technique which helps marketing user to target marketing campaigns more accurately and to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. The biclustering results can be tuned to find users’ browsing patterns relevant to current business problems. This paper presents a new application of biclustering to web usage data using a combination of heuristics and meta-heuristics algorithms. Two-way K-means clustering is used to generate the seeds from preprocessed web usage data, Greedy Heuristic is used iteratively to refine a set of seeds, which is fast but often yield local optimal solutions. In this paper, Genetic Algorithm is used as a global optimizer that can be coupled with greedy method to identify the global optimal target user groups based on their coherent browsing pattern. The performance of the proposed work is evaluated by conducting experiment on the msnbc, a clickstream dataset from UCI repository. Results show that the proposed work performs well in extracting optimal target users groups from the web usage data which can be used for focalized marketing campaigns.

scholarly journals Extraction of Knowledge from Web Server Logs Using Web Usage Mining

2019 ◽  
Vol 8 (S3) ◽  
pp. 12-15
Author(s):  
B. Harika ◽  
T. Sudha
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Server ◽  
Primary Source ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Usage ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

scholarly journals Personalised online sales using web usage data mining

2007 ◽  
Vol 58 (8-9) ◽  
pp. 772-782 ◽  
Author(s):  
Xuejun Zhang ◽  
John Edwards ◽  
Jenny Harding
Keyword(s):  
Data Mining ◽  
Web Usage ◽  
Online Sales ◽  
Usage Data

Web usage data mining agent

2002 ◽  
Author(s):  
Praveen Madiraju ◽  
Yanqing Zhang
Keyword(s):  
Data Mining ◽  
Web Usage ◽  
Usage Data

Extraction of Target User Group from Web Usage Data Using Evolutionary Biclustering Approach

2011 ◽  
Vol 2 (3) ◽  
pp. 69-79 ◽  
Author(s):  
R. Rathipriya ◽  
K. Thangavel ◽  
J. Bagyamani
Keyword(s):  
Data Mining ◽  
Greedy Heuristic ◽  
Data Mining Technique ◽  
Web Usage ◽  
Target User ◽  
Global Optimizer ◽  
User Groups ◽  
Optimal Target ◽  
Global Optimal ◽  
Usage Data

Data mining extracts hidden information from a database that the user did not know existed. Biclustering is one of the data mining technique which helps marketing user to target marketing campaigns more accurately and to align campaigns more closely with the needs, wants, and attitudes of customers and prospects. The biclustering results can be tuned to find users’ browsing patterns relevant to current business problems. This paper presents a new application of biclustering to web usage data using a combination of heuristics and meta-heuristics algorithms. Two-way K-means clustering is used to generate the seeds from preprocessed web usage data, Greedy Heuristic is used iteratively to refine a set of seeds, which is fast but often yield local optimal solutions. In this paper, Genetic Algorithm is used as a global optimizer that can be coupled with greedy method to identify the global optimal target user groups based on their coherent browsing pattern. The performance of the proposed work is evaluated by conducting experiment on the msnbc, a clickstream dataset from UCI repository. Results show that the proposed work performs well in extracting optimal target users groups from the web usage data which can be used for focalized marketing campaigns.

scholarly journals Process of Web Usage Mining to find Interesting Patterns from Web Usage Data

2012 ◽  
Vol 3 (1) ◽  
pp. 144-148 ◽  
Author(s):  
Ketul Patel ◽  
Dr. A. R. Patel
Keyword(s):  
Data Mining ◽  
Web Sites ◽  
Web Mining ◽  
World Wide ◽  
Pattern Analysis ◽  
Web Usage Mining ◽  
Huge Amount ◽  
Site Structure ◽  
Web Usage ◽  
Usage Data

The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to users’ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also presented Web Usage Mining applications and some Web Mining software.

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