CSE5230: Set Reading for Literature Review and Group Research Paper Topics 2004

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Reading For Individual Topics

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For each available topic, there is a list of set reading. Each student in a group preparing a paper on a particular topic must read the set reading for that topic, and include the articles in the set reading in their literature review.

Reading For All Topics

David J. Hand, Statistics and Data Mining: Intersecting Disciplines, ACM SIGKDD Explorations, 1, 1, pp. 16-19, June 1999.

Reading For Individual Topics

Association Rule Discovery

Rakesh Agrawal and Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules, In Jorge B. Bocca, Matthias Jarke and Carlo Zaniolo eds., VLDB'94, Proceedings of the 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, pp. 487-499, September 12-15 1994.
[PDF Version]
Marisa S. Viveros, John P. Nearhos and Michael J. Rothman, Applying Data Mining Techniques to a Health Insurance Information System, In T. M. Vijayaraman, Alejandro P. Buchmann, C. Mohan and Nandlal L. Sarda eds., Proceedings of the 22nd International Conference on Very Large Data Bases (VLDB), Mumbai, India, pp. 286-293, September 1996.

Back-propagation Neural Networks

David E. Rumelhart, Geoffrey E. Hinton and Ronald J. Williams, Learning Representations by Back-propagating Errors, Nature, 323, pp. 533-536, 1986.
K. A. Smith and J. N. D. Gupta, Neural Networks in Business: Techniques and Applications for the Operations Researcher, Computers and Operations Research, 27, 11, pp. 1023-1044, 2000.

Self-Organising Maps

Teuvo Kohonen, The self-organizing map, Proceedings of the IEEE, 78, 9, pp. 1464-1480, September 1990. (Invited paper)
Johan Himberg, Jussi Ahola, Esa Alhoniemi, Juha Vesanto and Olli Simula, The Self-Organizing Map as a Tool in Knowledge Engineering, In Nikhil R. Pal ed., Pattern Recognition in Soft Computing Paradigm, Soft Computing, pp. 38-65, World Scientific Publishing, 2001.
[PDF Version]
Marisa S. Viveros, John P. Nearhos and Michael J. Rothman, Applying Data Mining Techniques to a Health Insurance Information System, In T. M. Vijayaraman, Alejandro P. Buchmann, C. Mohan and Nandlal L. Sarda eds., Proceedings of the 22nd International Conference on Very Large Data Bases (VLDB), Mumbai, India, pp. 286-293, September 1996.

Decision Trees

J. R. Quinlan, Induction of Decision Trees, Machine Learning, 1, 1, pp. 81-106, 1986.
David D. Clarke, Richard Forsyth and Richard Wright, Machine learning in road accident research: Decision trees describing road-accidents during cross-flow turns, Ergonomics, 41, 7, pp. 1060-1079, 1998.

Clustering

A. K. Jain, M. N. Murty and P. J. Flynn, Data Clustering: A Review, ACM Computing Surveys, 31, 3, pp. 264-323, 1999.
Dharmendra S. Modha and W. Scott Spangler, Clustering hypertext with applications to web searching, In Proceedings of the eleventh ACM on Hypertext and hypermedia, San Antonio, TX, USA, pp. 143-152, May 30-jun 3 2000.

Bayesian Networks

Judea Pearl, Decision making under uncertainty, ACM Computing Surveys, 28, 1, pp. 89-92, 1996.
Russell J. Kennett, Kevin B. Korb and Ann E. Nicholson, Seabreeze Prediction Using Bayesian Networks, In Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01), Hong Kong, 2001, pp. 148-153.
[PDF Version]

Hidden Markov Models

L. R. Rabiner and B. H. Juang, An introduction to hidden Markov models, IEEE Magazine on Accoustics, Speech and Signal Processing, 3, 1, pp. 4-16, January 1986.
Weiqiang Lin, Mehmet A. Orgun and Graham J. Williams, Multilevels Hidden Markov Models for Temporal Data Mining, In Proceedings of the KDD 2001 Workshop on Temporal Data Mining (held in conjunction with the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)), San Francisco, CA, USA, August 26 2001.

Information Filtering

Nicholas J. Belkin and W. Bruce Croft, Information filtering and information retrieval: two sides of the same coin?, Communications of the ACM, 35, 12, pp. 29-38, December 1992. (special issue on information filtering)
Paul Graham, Better Bayesian Filtering, In Proceedings of the 2003 Spam Conference, Cambridge, MA, USA, January 17 2003.

Visualisation for Data Mining

Kurt Thearling, Barry Becker, Dennis DeCoste, Bill Mawby, Michel Pilote and Dan Sommerfield, Visualizing Data Mining Models, In Usama M. Fayyad, Georges G. Grinstein and Andreas Wierse eds., Information Visualization in Data Mining and Knowledge Discovery, 15, pp. 205-222, Morgan Kaufmann, 2001.
Daniel A. Keim, Information Visualization Techniques for Exploring Large Databases, In Fifth IFIP 2.6 Working Conference on Visual Database Systems, Fukuoka, Japan, May 10-12 2000. (Invited tutorial)

Ethics and Data Mining

Daniel E. O'Leary, Some Privacy Issues in Knowledge Discovery, IEEE Expert, 10, 2, pp. 48-52, April 1995.
Ljiljana Brankovic and Vladimir Estivill-Castro, Privacy Issues in Knowledge Discovery and Data Mining, In C. R. Simpson ed., Proceedings of the first Australian Institute of Computer Ethics International Conference on Computer Ethics, Melbourne, Australia, pp. 89-99, July 14-16 1999.

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