Study on online community user motif using web usage mining

The Web usage mining is the application of data mining, which is used to extract useful information from the online community. The World Wide Web contains at least 4.73 billion pages according to Indexed Web and it contains at least 228.52 million pages according Dutch Indexed web on 6th august 2015, Thursday. It's difficult to get needed data from these billions of web pages in World Wide Web. Here is the importance of web usage mining. Personalizing the search engine helps the web user to identify the most used data in an easy way. It reduces the time consumption; automatic site search and automatic restore the useful sites. This study represents the old techniques to latest techniques used in pattern discovery and analysis in web usage mining from 1996 to 2015. Analyzing user motif helps in the improvement of business, e-commerce, personalisation and improvement of websites.

[1]  Cheng Fang,et al.  Request Dependency Graph: A Model for Web Usage Mining in Large-Scale Web of Things , 2016, IEEE Internet of Things Journal.

[2]  Alexander S. Szalay,et al.  Ten Years of SkyServer I: Tracking Web and SQL e-Science Usage , 2014, Computing in Science & Engineering.

[3]  Maguelonne Teisseire,et al.  Real time Web usage mining: a heuristic based distributed miner , 2001, Proceedings of the Second International Conference on Web Information Systems Engineering.

[4]  Oren Etzioni,et al.  The World-Wide Web: quagmire or gold mine? , 1996, CACM.

[5]  Jeff Tian,et al.  Improving Web Navigation Usability by Comparing Actual and Anticipated Usage , 2015, IEEE Transactions on Human-Machine Systems.

[6]  Mohamed Jemni,et al.  Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[7]  Jiming Liu,et al.  Characterizing Web usage regularities with information foraging agents , 2004, IEEE Transactions on Knowledge and Data Engineering.

[8]  Steve G. Romaniuk Using Intelligent Agents to Identify Missing and Exploited Children , 2000, IEEE Intell. Syst..

[9]  Sung Ho Ha,et al.  Fuzzy Web Ad Selector Based on Web Usage Mining , 2003, IEEE Intell. Syst..

[10]  Siu Cheung Hui,et al.  Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis , 2012, IEEE Transactions on Affective Computing.

[11]  Brigitte Trousse,et al.  Advanced data preprocessing for intersites Web usage mining , 2004, IEEE Intelligent Systems.

[12]  Mamata Jenamani,et al.  Online Customized Index Synthesis in Commercial Web Sites , 2002, IEEE Intell. Syst..

[13]  Philip S. Yu,et al.  SpeedTracer: A Web Usage Mining and Analysis Tool , 1998, IBM Syst. J..

[14]  Sung Ho Ha,et al.  Helping Online Customers Decide through Web Personalization , 2002, IEEE Intell. Syst..

[15]  Huan Liu,et al.  An Unsupervised Feature Selection Framework for Social Media Data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[16]  Zhang Huiying,et al.  An intelligent algorithm of data pre-processing in Web usage mining , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[17]  Chabane Djeraba,et al.  Toward Recommendation Based on Ontology-Powered Web-Usage Mining , 2007, IEEE Internet Computing.

[18]  A. Joshi,et al.  Web mining: research and practice , 2004, Computing in Science & Engineering.

[19]  Ed Huai-hsin Chi Improving Web Usability Through Visualization , 2002, IEEE Internet Comput..

[20]  Georgios Paliouras,et al.  Personalizing Web Directories with the Aid of Web Usage Data , 2010, IEEE Transactions on Knowledge and Data Engineering.

[21]  Ajith Abraham,et al.  Web usage mining using artificial ant colony clustering and linear genetic programming , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[22]  Anil K. Jain,et al.  Online handwritten script recognition , 2004 .

[23]  Chien Chin Chen,et al.  An Unsupervised Approach for Person Name Bipolarization Using Principal Component Analysis , 2012, IEEE Transactions on Knowledge and Data Engineering.

[24]  Balaji Padmanabhan,et al.  GHIC: a hierarchical pattern-based clustering algorithm for grouping Web transactions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[25]  Georgios Paliouras,et al.  From Web usage statistics to Web usage analysis , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).