On the Existence and Significance of Data Preprocessing Biases in Web-Usage Mining

References

  • Baird D.Experimentation: An Introduction to Measurement Theory and Experiment Design (1994) (Prentice Hall, Englewood Cliffs, NJ) Google Scholar
  • Berendt B., Mobasher B., Spiliopoulou M., Wiltshire J. Measuring the accuracy of sessionizers for web usage analysis. Workshop on Web Mining at the 2001 SIAM Conference on Data Mining (2001) 7–14Google Scholar
  • Berry M., Linhoff G.Mastering Data Mining: Art and Science of Customer Relationship Management (1999) (John Wiley and Sons, New York) Google Scholar
  • Brodley C., Kohavi R. KDD-Cup 2000 Organizers' Report: Peeling the onion. SIGKDD Explorations (2000) 2:1–8Google Scholar
  • Buchner A., Mulvenna M. Discovering Internet marketing intelligence through online analytical web usage mining. ACMSIGMOD Record (1999) 27:57–61Google Scholar
  • Cabena P.Discovering Data Mining: From Concept to Implementation (1997) (Prentice Hall, Inc., Upper Saddle River, NJ) Google Scholar
  • Cooley R., Mobasher B., Srivastava J. Data preparation for mining World Wide Web browsing patterns. Knowledge and Inform. Systems (1999) 1:5–31CrossrefGoogle Scholar
  • Cross M. Introduction to chaos. (1998) . Lecture Notes in Theoretical Physic, Dept. of Physics, Cal. Institute of Technology. http://www.cmp.caltech.edu/~mcc/chaos_new/Lorenz.htmlGoogle Scholar
  • Cunha C., Jaccoud C. Determining WWW user's next access and its application to pre-fetching. Internat. Sympos. Comput. and Comm. ‘97 (1997) (Alexandria, Egypt)33–42Google Scholar
  • Cutler M. E-Metrics: Tomorrow's business metrics today. Proc. of the Sixth ACM SIGKDD Internat. Conf. KDD (2000) KDD 2000, Boston, MA:12–20CrossrefGoogle Scholar
  • Dradley L. Chaos and fractal. (2001) . Intermediate Physics Seminar, Dept. of Physics, Johns Hopkins University, Baltimore MD. http://www.pha.jhu.edu/~ldb/seminar/butterfly.htmlGoogle Scholar
  • Fader P., Hardie B. Forecasting repeat sales at cdnow: A case study. Interfaces (1999) 31:94–107CrossrefGoogle Scholar
  • Fayyad U., Shapiro G., Smyth P., Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. From data mining to knowledge discovery: An overview. Advances in Knowledge Discovery and Data Mining (1996) (MIT Press, Cambridge, MA) 20–42Google Scholar
  • Fitzsimons G., Williams P. Asking questions can change choice behavior: Does it do so automatically or effortfully? J. of Exper. Psych. Appl. (2000) 6:195–206CrossrefGoogle Scholar
  • Gleick J.Chaos—Making a New Science (1987) (Mountain Man Graphics, Newport Beach, Australia) Google Scholar
  • Glymour C. Statistical themes and lessons for data mining. Data Mining and Knowledge Discovery (1997) 1:11–28CrossrefGoogle Scholar
  • Hughes A. M.The Complete Database Marketing (1996) (Irwin Professional, Chicago, IL) Google Scholar
  • Johnson E., Moe W., Fader P., Bellman S., Lohse J. On the depth and dynamics of online search behaviour. (2000) . The Wharton School Working Paper #00-014, University of Pennsylvania, Philadelphia, PAGoogle Scholar
  • Johnson R., Wichern D.Applied Multivariate Statistical Analysis (1998) (Prentice Hall, Englewood Cliffs, NJ) 697–703Google Scholar
  • Khabaza T. As E-asy as falling off a web log: Data mining hits the web. SPSS Data Mining (2001) 22:12–24Google Scholar
  • Kimbrough S., Padmanabhan B., Zheng Z. On usage metric for determining authoritative sites. Proc. of World Inform. Tech. 2000 (2000) Brisbane, Australia:23–32Google Scholar
  • Korgaonkar P., Wolin L. D. A multivariate analysis of web usage. J. Advertising Res. (1999) 39:53–68Google Scholar
  • Ling C., Li C. Data mining for direct marketing: Problems and solutions. Proc. of the Fourth Internat. Conf. on Knowledge Discovery and Data Mining 98 (1998) 73–79Google Scholar
  • Lorenz E. Deterministic nonperiodic flow. J. Atmosphere Sci. (1963) 20:130–141CrossrefGoogle Scholar
  • Mena J.Data Mining Your Website (1999) (Digital Press, Boston, MA) Google Scholar
  • Mobasher B., Cooley R., Srivastava J. Automatic personalization based on web usage mining. (1999) . Working Paper TR 99-010, Department of Computer Science, Depaul University, Chicago, ILGoogle Scholar
  • Mobasher B., Dai H., Luo T., Nakagawa M. Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery (2002) 6:61–82CrossrefGoogle Scholar
  • Moe W., Fader P. Which visits lead to purchases? Dynamic conversion behavior at e-commerce sites. (2000) . Working Paper #00-023, the Wharton School, University of Pennsylvania, Philadelphia, PAGoogle Scholar
  • Novak T., Hoffman D. New metrics for new media: Toward the development of web measurement standards. World Wide Web J (1997) 2:213–246Google Scholar
  • Padmanabhan B., Zheng Z., Kimbrough S. A comparison of site-centric and user-centric data mining approaches to predicting session-level purchase behavior on the web. (2001a) . Working Paper 01-2001, Department of Operations and Information Management, the Wharton School, University of Pennsylvania, Philadelphia, PAGoogle Scholar
  • Padmanabhan B., Zheng Z., Kimbrough S. Personalization from incomplete data: What you don't know can hurt. Proc. of the Seventh ACM SIGKDD Internat. Conf. on KDD 2001 (2001b) San Francisco, CA:154–163CrossrefGoogle Scholar
  • Pitkow J. Summary of WWW characterizations. Comput. Networks and ISDN Systems (1998) 30:551–558CrossrefGoogle Scholar
  • Sen S., Padmanabhan B., Tuzhilin A., White N., Stein R. The identification and satisfaction of consumer analysis-driven information needs of marketers on the WWW. Eur. J. of Marketing (1998) 32:688–702CrossrefGoogle Scholar
  • Srivastava J., Cooley R., Deshpande M., Tan P. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations (2000) 1:12–23CrossrefGoogle Scholar
  • Theusinger C., Huber K. Analyzing the footsteps of your Customers. Proc. of the Sixth ACM SIGKDD Internat. Conf. on Web KDD 2000 (2000) Boston, MA:44–52Google Scholar
  • Tversky A., Kahneman D. The framing of decisions and the psychology of choice. Science (1981) 211:453–458CrossrefGoogle Scholar
  • VanderMeer D., Dutta K., Datta A. Enabling scalable online personalization on the web. Proc. of Electronic Commerce (EC00)/ ACM (2000) Minneapolis, MN:185–196CrossrefGoogle Scholar
  • Wu K., Yu P., Ballman A. SpeedTracer: A web usage mining and analysis tool. Internet Comput (1999) 37:89–105Google Scholar
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