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MEME*: An Adaptive Email-based Knowledge Sharing System for Educational Institutions

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Advanced Computer Systems

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 664))

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Abstract

Messaging systems are essential for circulating information within an organization, yet currently the email-based exchange of information does not take advantage of technologies that could change several aspects of the way people share information and knowledge. In this paper we discuss basic functionality and technologies for an adaptive messaging system. Potentially, messaging systems could be utilized in a number of different modes. They help users to distribute information based on certain explicit or implicit criterions. They can help to find the recipient or a group of recipients for a message to be sent. Proactive information delivery based on information semantics and user profiles is another feature. In this paper we concentrate on just one component of the system — clustering of users based on their educational background.

*Meme: Function: noun. An idea, behavior, style or usage that spreads from person to person within a culture. Source: New Dictionary Search, 2000, Merriam-Webster Incorporated

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References

  1. Baker D., L., McCallum A., K., “Distributional Clustering of Words for Text Classification”, in B. Croft., A. Moffat, C. J. van Rijsbergen, R. Wilkinson, J. Zobel (eds.), Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM Press, 1998, pp. 96–103

    Google Scholar 

  2. Brzezinski J., R., Knafl G., J., “Logistic Regression Modeling for Context-Based Classification”, in proceedings of the Internet Data Management Conference (IDM’99), Florence, Italy, 1999

    Google Scholar 

  3. Brzezinski J., R, Knafl G., J., “Logistic Regression and Singular Value Decomposition Pre-processing for Classification of Documents” (co-author) in Proceedings of the Advanced Computer Systems Conference (ACS’99), Technical University of Szczecin Press, Szczecin, Poland, 1999

    Google Scholar 

  4. Brzezinski J., R., Knafl G., J., “Data Mining Approach to Document Classification Using Logistic Regression”, in Proceedings of the CTI Research Conferece, DePaul University, Chicago, 1999

    Google Scholar 

  5. Brzezinski J., R., “Logistic regression for classification of text documents”, PhD. dissertation, DePaul Univesity, School of Computer Science, Telecommunications and Information Systems, 2000.

    Google Scholar 

  6. Florek, K., Lukaszewicz, J., Perkal, J., and Zubrzycki, S. (1951a), “Sur la Liaison et la Division des Points d’un Ensemble Fini,” Colloquium Mathematicae, 2, 282–285. Florek, K., Lukaszewicz, J., Perkal, J, and Zubrzycki, S. (1951b), “Taksonomia Wroclawska,” Przeglad Antropol., 17, 193-211.

    Google Scholar 

  7. Fraley C., Raftery A., E., “How many clusters? Which clustering method? — Answers via model-based cluster analysis”, 1998, Department of Statistics, University of Washington: Technical Report no. 329.

    Google Scholar 

  8. Fritzche M., “Automatic clustering techniques in information retrieval”, Diplomarbeit, Institut fuer Informatik der Universitaet Stuttgart, 1973

    Google Scholar 

  9. Kaufman L., Rousseeuw P., I, “Finding groups in data: An introduction to cluster analysis”, NY, 1990, John Wiley and Sons, Inc.

    Google Scholar 

  10. Loh W., Y., Shih Y., S., “Split selection methods for classification trees”, Statistica Sinica analysis (with discussion), Journal of the American Statistical Association, 83:715–728,1997

    Google Scholar 

  11. Massart, D.L. and Kaufman, L. (1983), The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis, New York: John Wiley & Sons, Inc.

    Google Scholar 

  12. “Neural Computing. NeuralWoeks Professional II/PLUS and NeuralWorks Explorer”, Neural Ware, Inc., Technical Publications Group, 1991

    Google Scholar 

  13. Quinlan J., 1993, “C4.5 Programs for Machine Learning”, San Mateo, CA: Morgan Kaufmann, 1993

    Google Scholar 

  14. [Ramoni and Sebastiani, 1999] Ramoni M., Sebastiani P., “An Introduction to the Robust Bayesian Classifier”, Tech Report Kmi-TR-79, Knowledge Media Institute, The Open Institute, Milton Keynes, UK, 1999

    Google Scholar 

  15. Saltan G, Automatic Text Processing. The transformation, Analysis and Retrieval of Information by Computer, Addison-Wesley Publishing Company, 1989

    Google Scholar 

  16. Ward, J.H. (1963), “Hierarchical Grouping to Optimize an Objective Function,” Journal of the American Statistical Association, 58, 236–244.

    Article  MathSciNet  Google Scholar 

  17. Wong, M.A. and Lane, T. (1983), “A kth Nearest Neighbor Clustering Procedure,” Journal of the Royal Statistical Society, Series B, 45, 362–368.

    MathSciNet  MATH  Google Scholar 

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Jerzy Sołdek Jerzy Pejaś

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Brzezinski, J., Dain, M. (2002). MEME*: An Adaptive Email-based Knowledge Sharing System for Educational Institutions. In: Sołdek, J., Pejaś, J. (eds) Advanced Computer Systems. The Springer International Series in Engineering and Computer Science, vol 664. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8530-9_15

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  • DOI: https://doi.org/10.1007/978-1-4419-8530-9_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4635-7

  • Online ISBN: 978-1-4419-8530-9

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