A Proposal of Some Analysis Methods for Discovery of User Information from Web Data

  • Ahn, JeongYong (Division of Computer Science and Information Communications, Seonam University) ;
  • Han, Kyung Soo (Division of Mathematics and Statistical Information, Chonbuk National University)
  • Published : 2001.04.01

Abstract

The continuous growth in the use of the World Wide Web is creating the data with very large scale and different types. Analyzing such data can help to determine the life time value of users, evaluate the effectiveness of web sites, and design marketing strategies and services. In this paper, we propose some analysis methods for web data and present an example of a prototypical web data analysis.

Keywords

References

  1. Proceedings of the workshop on Web Usage Analysis and User Profiling(WebKDD'99) Borges,J.;Levene,M.
  2. Proceedings of the workshop on Web Usage Analysis and User Profiling(WebKDD'99) Navigation Pattern Discovery from Internet Data Buchner,A.G.;Baumgarten,M.;Anand,S.S.;Mulvenna,M.D.;Hughes,J.G.
  3. PhD Dissertation, University of Minnesota Web Usage Mining:Discovery and Application of Interesting Patterns from Web Data Cooley,R.W.
  4. Intelligent Data Analysis v.1 no.1 Data Preprocessing and Intelligent Data Analysis Famili,A.;Shen,W.M.;Weber,R.;Simondis,E.
  5. Proceedings of the International Conference on the Interface:Computing Science and Statistics Data Mining and Statistics:What's the Connection? Friedman,J.H.
  6. Proceedings of the workshop on Web Information and Data Management Data Mining and the Web:Past, Present and Future Garofalakis,M.N.;Rastogi,R.;Seshadri,S.;Shim,K.S.
  7. Proceedings of ACM SIGMOD International Conference on Management of Data CURE:An Efficient Clustering Algorithm for Large Databases Guha,S.;Rastogi,R.;Shim.K.S.
  8. Intelligent Data Analysis v.2 no.2 Intelligent Data Analysis:Issues and Opportunities Hand.D.J.
  9. Applied Statistics v.28 Algorithm AS 136:A K-means clustering algorithm hartigan,J.A.;Wong,M.A.
  10. COMPSTAT(Proceedings in Computational Statistics Huge Data Sets Huber,P.J.
  11. Self-Organization and Associative Memory Kohonen,T.
  12. Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining Mining Access Patterns Efficiently from Web Logs Pei,J.;Han,J.;Mortazavi-asl,B.;Zhu,H.
  13. Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases(PKDD'2000) Data Analysis for web marketing and merchandizing applications Spiliopoulou,M.
  14. SIGKDD Explorations v.1 no.Issue 2 Web Usage Mining:Discovery and Applications of Usage Patterns from Web Data Srivastava,J.;Cooley,R.W.;Deshpande,M.;Tan,P.N.
  15. Proceedings of ACM SIGMOD International Conference on Data Management BIRCH:An Efficient Data Clustering Method for Very Large Databases Zhang,T.;Ramarkrishnan,R.;Livny,M.