Machine learning for information architecture in a large governmental website

Proceedings of the ACM IEEE International Conference on Digital Libraries, JCDL 2004

Volumn , Issue , 2004, Pages 151-159

  Efron, Miles ^a   Elsas, Jonathan ^a   Marchionini, Gary ^a   Zhang, Junliang ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Information Architecture; Interface Design; Machine Learning

Indexed keywords

ALGORITHMS; COMPUTER ARCHITECTURE; DIGITAL LIBRARIES; HUMAN COMPUTER INTERACTION; INFORMATION RETRIEVAL; INTERFACES (COMPUTER); SEMANTICS; SET THEORY; WEBSITES;

CLUSTERING; DYNAMIC INTERFACES; INFORMATION ARCHITECTURE; INTERFACE DESIGN;

LEARNING SYSTEMS;

EID: 4944259893     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/996350.996386     Document Type: Conference Paper

References (18)

1. A. Agresti. An Introduction to Categorical Data Analysis. Wiley, New York, 1996.
2. C. Ahlberg, C. Williamson, and B. Shneiderman. Dynamic queries for information exploration: an implementation and evaluation. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 619-626, 1992.
3. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM Press, 1999.
4. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the eleventh annual conference on Computational learning theory, pages 92-100. ACM Press, 1998.
5. H. Chen and S. Dumais. Hierarchical classification of web content. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pages 256-263, 2000.
6. M. Efron, G. Marchionini, and J. Zhang. Implications of the recursive representation problem for automatic concept identification in on-line governmental information. In Proceedings of the ASIST Special Interest Group on Classification Research (ASIST SIG-CR), 2003.
7. C. Fraley and A. E. Raftery. How many clusters? which clustering method? answers via model-based cluster analysis. The Computer Journal, 41(8):578-588, 1998.
8. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: a review. ACM Computing Surveys, 31(3):264-323, September 1999.
9. T. Joachims. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In D. H. Fisher, editor, Proceedings of ICML-97, 14th International Conference on Machine Learning, pages 143-151, Nashville, US, 1997. Morgan Kaufmann Publishers, San Francisco, US.
10. T. Joachims. Text categorization with support vector machines: learning with many relevant features. In C. Nédellec and C. Rouveirol, editors, Proceedings of ECML-98, 10th European Conference on Machine Learning, pages 137-142, Chemnitz, DE, 1998. Springer Verlag, Heidelberg, DE.
11. I. T. Jolliffe. Principal Component Analysis. Springer, 2nd edition, 2002.
12. L. Kaufman and P. J. Rosseeuw. Finding Groups in Data: an Introduction to Cluster Analysis, Wiley, 1990.
13. G. Marchionini and B. Brunk. Toward a general relation browser: a GUI for information architects. Journal of Digital Information, 4(1), 2003. http://jodi.ecs.soton.ac.uk/Articles/v04/i01/Marchionini/.
14. A. K. McCallum. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.emu.edu/~mccallum/bow, 1996.
15. T. Mitchell. Machine Learning. McGraw Hill, 1997.
16. E. Rasmussen. Clustering algorithms. In W. B. Frakes and R. Baeza-Yates, editors, Information Retrieval: Data Structures and Algorithms, pages 419-442. Prentice Hall, 1992.
17. R. Tibshirani, G. Walther, and T. Hastie. Estimating the number of clusters in a dataset via the gap statistic, 2000. http://citeseer.nj.nec.com/ tibshirani00estimating.html.
18. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 2000.