EE 380L : Practicum in Data Mining Spring 2003 Prof. Joydeep Ghosh

Reading List
EE 380L - A Practicum in Data Mining
Spring 2003

Notices

• Be aware of the copyright notice when you use these materials.

Paper Selection Policy

• Each  student must select and present any two of the following papers (except the general reading papers). Both papers should not be on the same sub-topic. 
• Paper is allocated on first come basis.
• To select a paper, email your selection to the TA.
• Scheduling of your talk depends on the topic you have chosen. Ideally it will take place in the "Student Paper Presentations" slot (in the course schedule) right after that topic has been covered in class.
• Selected means the paper has been selected for presentation and cannot be selected again. Also check for pending requests on the following page.
• Please use this link to request a paper for presentation and edit the required fields of the subject line. This queues your request on this page instantly.

I. General Reading (but not for class presentations) supplement to the 7 papers in your course packet.

1. Information retrieval on the web
   Mei Kobayashi and Koichi Takeda
   ACM Computing Surveys, vol.32, no.2, 144-173, 2000
2. Data mining for hypertext: A tutorial survey S. Chakrabarti
   ACM SIGKDD Explorations, 1(2), 1-11, 2000
3. Impact of Similarity Measures on Web-page Clustering
   A.Strehl, J. Ghosh and R. Mooney
   Proc. AAAI workshop on AI for Web Search, K. Bollacker (Ed)
   TR WS-00-01, AAAI Press, July 2000, pp. 58-64
4. Data Preparation for Mining World Wide Web Browsing Patterns
   Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava
   Knowledge and Information Systems, V1(1), 1999
5. An Internet-enabled Knowledge Discovery Process
   by Alex Buchner, et. al., MINEit Software Ltd., 1999

II. Hyperlinks

1. Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery
   Soumen Chakrabarti, Martin van den Berg, Byron Dom, WWW8
   Selected: Tabassum
2. Random Walks with "Back Buttons"
   Ronald Fagin et. al., Proc. 2000 ACM Symposium on Theory of Computation
3. Stochastic models for the Web graph
   R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, and Eli Upfal Proc. of the 41th IEEE Symp. on Foundations of Computer Science.2000
   Selected: Rajalingam Alagesan : 3/4/03
4. "Winners Don't Take All: Characterizing the Competition for Links on the Web,"
   D. Pennock, G.W. Flake, S. Lawrence, E.J. Glover, C.L. Giles Proceedings of the National Academy of Sciences, 99(8),5207-5211, April 2002.
   Selected:Kunal
5. Learning Probabilistic Models of Link Structure
   Lisa Gentoor, Nir Friedman, Daphne Koller etal. JMLR. 3(Dec) 2002

III. Information Retrieval

1. Hierarchical Bayesian models for applications in information retrieval
   David Blei, Michael Jordan and Andrew Y. Ng Bayesian Statistics 7, (Oxford University Press) 2003
   Selected: Andromache Howe : 3/4/03
2. A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections
   Alexei Vinokourov and Mark Girolami
   BUBL journals: Information Processing and Management, 2002
   Selected: Matt MacMahon : 3/6/03
3. Latent Semantic Kernels
   Nello Cristianini and Huma Lodhi and John Shawe-Taylor
   Journal of Intelligent Information Systems, 18:2/3, 127-152, 2002
   Selected: Hyuk Cho : 3/6/03
4. Learning Approaches for Detecting and Tracking News Events
   Y. Yang et. al., IEEE Intelligen Systems, 14(4):32--43, 1999
   Selected: Kunal : 3/18/03
5. Text Classification in a Hierarchical Mixture Model for Small Training Sets
   Kristina Toutanova, Francine Chen, Kris Popat, and Thomas Hofmann
   Proceedings of the Tenth International ACM Conference on Information and Knowledge Management, CIKM 2001
   Selected: Vidya Narayan : 3/18/03
6. Topic Distillation at TrecWeb2002
   Tsinghua, City Univ. London, IBM Haifa.
   Selected: Abhinav Sharma 4/17/03
7. Name Page finding ate TrecWeb2002: Top3 ( Tsinghua, CMU, Glasgow )

IV. Contents + Links

1. The missing link - a probabilistic model of document content and hypertext connectivity
   David Cohn and Thomas Hofmann, NIPS-13, 2001
   Selected: Hyuk Cho :3/27/03
2. Categorization of web pages and user clustering with mixture of hidden Markov models
   A.Ypma and T.Heskes WEBKDD'02 pp 31-43
   Selected: Matt MacMahon : 3/27/03
3. The impact of site structure and user environment on session reconstruction in Web usage analysis
   B.Masand , M.Spiliopoulou, J.Srivastava etal Working Notes of the Fourth WebKDD Web Mining for Usage Profiles, Workshop at KDD, pp 115-129, 2002
   Selected: Alexander Y. Liu : 4/17/03
4. Stumme, G., Hotho, A., & Berendt, B. (2002). Usage Mining for and on the Semantic Web. In Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, Nov. 1-3, 2002. Selected Tabassum : 4/22/03
5. Shaping the Web: Why the politics of search engines matters
   Lucas D. Introna and Helen Nissenbaum.
   Selected: Vidya Venkat : 3/20/03

V. Personalization

1. LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition (2002)
   Ed H. Chi, Adam Rosien, Jeffrey Heer WebKDD02
   Selected: Rajalingam Alagesan : 4/22/03
2. ]Efficient and Anonymous Web-Usage Mining for Web Personalization
   Cyrus Shahabi, Farnoush Banaei-Kashani
   Selected: Abhinav Sharma: 4/24/03
3. A critical View of Recommendor Systems
   Andreas Mild and Martin Natter Working Paper No.82 July 2001
   Selected: Alexander Y. Liu: 4/24/03
4. Generative Models for Cold-Start Recommendations
   Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar and David M. Pennock
   SIGIR-01 Workshop on Recommender Systems
5. PVA: A Self-Adaptive Personal View Agent
   Chien Chin Chen, Meng Chang Chen and Yeali Sun
   Journal of Intelligent Information Systems, 18:2/3, 173-194, 2002