Web mining
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Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web. It makes utilization of automated apparatuses to reveal and extricate data from servers and web2 reports, and it permits organizations to get to both organized and unstructured information from browser activities, server logs, website and link structure, page content and different sources.
The goal of Web structure mining is to generate structural summary about the Web site and Web page. Technically, Web content mining mainly focuses on the structure of inner-document, while Web structure mining tries to discover the link structure of the hyperlinks at the inter-document level. Based on the topology of the hyperlinks, Web structure mining will categorize the Web pages and generate the information, such as the similarity and relationship between different Web sites.
Web structure mining can also have another direction – discovering the structure of Web document itself. This type of structure mining can be used to reveal the structure (schema) of Web pages, this would be good for navigation purpose and make it possible to compare/integrate Web page schemes. This type of structure mining will facilitate introducing database techniques for accessing information in Web pages by providing a reference schema.
External links
- The Future of Web Sites = Web Services – (with a section on web scraping)
Books
- Zdravko Markov, Daniel T. Larose "Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage", Wiley, 2007
- Jesus Mena, "Data Mining Your Website", Digital Press, 1999
- Soumen Chakrabarti, "Mining the Web: Analysis of Hypertext and Semi Structured Data", Morgan Kaufmann, 2002
- Bing Liu, "Web Data Mining: Exploring Hyperlinks, Contents and Usage Data", Springer, 2007
- Advances in Web Mining and Web Usage Analysis 2005 - revised papers from 7 th workshop on Knowledge Discovery on the Web, Olfa Nasraoui, Osmar Zaiane, Myra Spiliopoulou, Bamshad Mobasher, Philip Yu, Brij Masand, Eds., Springer Lecture Notes in Artificial Intelligence, LNAI 4198, 2006
- Web Mining and Web Usage Analysis 2004 - revised papers from 6 th workshop on Knowledge Discovery on the Web, Bamshad Mobasher, Olfa Nasraoui, Bing Liu, Brij Masand, Eds., Springer Lecture Notes in Artificial Intelligence, 2006
- Mike Thelwall, "Link Analysis: An Information Science Approach", 2004, Academic Press
Bibliographic references
- Baraglia, R. Silvestri, F. (2007) "Dynamic personalization of web sites without user intervention", In Communications of the ACM 50(2): 63-67
- Cooley, R. Mobasher, B. and Srivastave, J. (1997) “Web Mining: Information and Pattern Discovery on the World Wide Web” In Proceedings of the 9th IEEE International Conference on Tool with Artificial Intelligence
- Cooley, R., Mobasher, B. and Srivastava, J. “Data Preparation for Mining World Wide Web Browsing Patterns”, Journal of Knowledge and Information System, Vol.1, Issue. 1, pp. 5–32, 1999
- Costa, RP and Seco, N. “Hyponymy Extraction and Web Search Behavior Analysis Based On Query Reformulation”, 11th Ibero-American Conference on Artificial Intelligence, 2008 October.
- Kohavi, R., Mason, L. and Zheng, Z. (2004) “Lessons and Challenges from Mining Retail E-commerce Data” Machine Learning, Vol 57, pp. 83–113
- Lillian Clark, I-Hsien Ting, Chris Kimble, Peter Wright, Daniel Kudenko (2006)"Combining ethnographic and clickstream data to identify user Web browsing strategies" Journal of Information Research, Vol. 11 No. 2, January 2006
- Eirinaki, M., Vazirgiannis, M. (2003) "Web Mining for Web Personalization", ACM Transactions on Internet Technology, Vol.3, No.1, February 2003
- Mobasher, B., Cooley, R. and Srivastava, J. (2000) “Automatic Personalization based on web usage Mining” Communications of the ACM, Vol. 43, No.8, pp. 142–151
- Mobasher, B., Dai, H., Luo, T. and Nakagawa, M. (2001) “Effective Personalization Based on Association Rule Discover from Web Usage Data” In Proceedings of WIDM 2001, Atlanta, GA, USA, pp. 9–15
- Nasraoui O., Petenes C., "Combining Web Usage Mining and Fuzzy Inference for Website Personalization", in Proc. of WebKDD 2003 – KDD Workshop on Web mining as a Premise to Effective and Intelligent Web Applications, Washington DC, August 2003, p. 37
- Nasraoui O., Frigui H., Joshi A., and Krishnapuram R., “Mining Web Access Logs Using Relational Competitive Fuzzy Clustering”, Proceedings of the Eighth International Fuzzy Systems Association Congress, Hsinchu, Taiwan, August 1999
- Nasraoui O., “World Wide Web Personalization,” Invited chapter in “Encyclopedia of Data Mining and Data Warehousing”, J. Wang, Ed, Idea Group, 2005
- Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos C. D. (2003) “Web usage mining as a tool for personalization: a survey”, User modelling and user adapted interaction journal, Vol.13, Issue 4, pp. 311–372
- I-Hsien Ting, Chris Kimble, Daniel Kudenko (2005) "A Pattern Restore Method for Restoring Missing Patterns in Server Side Clickstream Data"
- I-Hsien Ting, Chris Kimble, Daniel Kudenko (2006) "UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to improve a Web Site’s Design"
- Weichbroth, P., Owoc, M., Pleszkun, M. (2012) "Web User Navigation Patterns Discovery from WWW Server Log Files"
- Madria S.K., Bhowmick S.S., Ng W.K., Lim E.P. (1999) Research Issues in Web Data Mining. In: Mohania M., Tjoa A.M. (eds) DataWarehousing and Knowledge Discovery. DaWaK 1999. Lecture Notes in Computer Science, vol 1676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48298-9_32.