國立台灣科技大學 資訊工程系所

智慧型系統實驗室


研 究 資 源


Intelligent Systems on the Web (Data Mining)


Tutorial/Survey


1. HO Tu Bao, "INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING", Institute of Information Technology,National Center for Natural Science and Technology.

2. Ming-Syan Chen; Jiawei Han; Yu, P.S., "Data mining: an overview from a Data-base perspective", Knowledge and Data Engineering,IEEE Tran on,Vol 8 Issue 6,Dec,1996 ,P866-883

3. Two Crows Corporation Introduction to Data Mining and Knowledge Discovery,Third Edition.

 4. Jiawei Han ; Miceline Lamber, "Data Mining:Concepts and Techniques", Morgan Kaufmann, 2000

5. R. Kosala and H. Blockeel, "Web Mining Research: A Survey" ACM of SIGKDD Explorations, Vol. 2, pp. 1-15, 2001


6. M. Spiliopoulou, "Data mining for the web", In Principles of Data Mining and Knowledge Discovery, Second European Symposium, PKDD ’99, pages 588-589, 1999.

7. Jaideep Srivastava, Robert Cooley,et, "Web Usage Mining :Discovery and Application of Usage Patterns from Web Data", ACM SIGKDD Volume 1,Issue 2,2000

8. W.M.P. van der Aalst, B.F. van Dongen, J. Herbst, L. Marustera, G. Schimm, A.J.M.M. Weijters, “Workflow mining: A survey of issues and approaches”, Data & Knowledge Engineering, Volume.47, Issue 2, November 2003, Pages:237 - 267 12. Lindsay I Smith, “A tutorial on Principal Components Analysis”, February 26,2002

9. Srivatsan Laxman and P. S. Sastry, "A survey of temporal data mining", Sadhana, Volume 31, Number 2 , 2006 apr,173-198p

Special Issue


1.Association Rules and Time Sequence Analysis http://www.cs.wmich.edu/~yang/teach/cs595/slides/Association/index.htm

2.Clustering Algorithm for Data Mining http://ntudlm.ntu.edu.tw/~cychen/olddoc/ClusteringDataMining.html

3.A SURVEY OF ASSOCIATION RULES http://www.cs.uh.edu/~ceick/6340/grue-assoc.pdf

4.S. Chakrabarti;Data mining for hypertext: A tutorial survey ACM SIGKDD Explorations, 1(2), pages 1--11, 2000 http://lans.ece.utexas.edu/course/ee380l/2001sp/readinglist/soumen_survey.ps.gz

5. R. Kosala and H. Blockeel:Web Mining Reseach SIGKDD Explorations, June 2000. Volume 2, Issue 1 http://lans.ece.utexas.edu/course/ee380l/2001sp/readinglist/kosala.pdf

6.S. Chakrabarti Data mining for hypertext: A tutorial survey ACM SIGKDD Explorations, 1(2), pages 1--11, 2000 投影片資料 http://acm.org/pubs/citations/proceedings/ai/349093/p1-chakrabarti/

7.A. K. Jain, M. N. Murty and P. J. Flynn Data clustering: a review ACM Comput. Surv. 31, 3 (Sep. 1999), Pages 264 - 323 http://acm.org/pubs/citations/journals/surveys/1999-31-3/p264-jain/

8.Xiaobin Fu, Jay Budzik and Kristian J. Hammond Mining navigation history for recommendation Proceedings of the 2000 international conference on Intelligent user interfaces ,2000, Pages 106 - 112 http://acm.org/pubs/citations/proceedings/uist/325737/p106-fu/

 

9.Association Rules and Time Sequence Analysis http://www.cs.wmich.edu/~yang/teach/cs595/slides/Association/index.htm

10.Clustering Algorithm for Data Mining http://ntudlm.ntu.edu.tw/~cychen/olddoc/ClusteringDataMining.html

11.A SURVEY OF ASSOCIATION RULES http://www.cs.uh.edu/~ceick/6340/grue-assoc.pdf

12.S. Chakrabarti;Data mining for hypertext: A tutorial survey ACM SIGKDD Explorations, 1(2), pages 1--11, 2000 http://lans.ece.utexas.edu/course/ee380l/2001sp/readinglist/soumen_survey.ps.gz

13. R. Kosala and H. Blockeel:Web Mining Reseach SIGKDD Explorations, June 2000. Volume 2, Issue 1 http://lans.ece.utexas.edu/course/ee380l/2001sp/readinglist/kosala.pdf

14.S. Chakrabarti Data mining for hypertext: A tutorial survey ACM SIGKDD Explorations, 1(2), pages 1--11, 2000 投影片資料 http://acm.org/pubs/citations/proceedings/ai/349093/p1-chakrabarti/

15. A. K. Jain, M. N. Murty and P. J. Flynn Data clustering: a review ACM Comput. Surv. 31, 3 (Sep. 1999), Pages 264 - 323 http://acm.org/pubs/citations/journals/surveys/1999-31-3/p264-jain/

16. Xiaobin Fu, Jay Budzik and Kristian J. Hammond Mining navigation history for recommendation Proceedings of the 2000 international conference on Intelligent user interfaces ,2000, Pages 106 - 112 http://acm.org/pubs/citations/proceedings/uist/325737/p106-fu/

17. Raymond T. Ng and Jian Pei, "Introduction to the special issue on data mining for health informatics", ACM SIGKDD Explorations Newsletter, Volume 9 ,  Issue 1  (June 2007), Pages: 1 – 2(url待補)

18. Tao Li and Chang-Shing Perng and Sheng Ma,"Guest editorial: special issue on temporal data mining: theory, algorithms and applications", Data Mining and Knowledge Discovery, Volume 16, Number 1, 2008 Feb,  1-3p(url待補)

Paper Collection


1. Yo-Ping Huang* and Yen-Chun Lee, An Intelligent Approach to Mining the Desired and Related Websites, ICS, 221

2. Chun-Min Hung, Yueh-Min Huang, Tse-Sheng Chen, Assessing Check Credit With Skewed Data: A Knowledge Discovery Case Study, IGS, 136

3. Z.-H. Zhou, Three perspectives of data mining, Artificial Intelligence, Volume 143, Issue 1, Jan-2003

4. G. Widmer, Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries, Artificial Intelligence Vol 146, Issue 2, Jun-2003

5. K.S. Candan, W.-S. Li, Reasoning for Web document associations and its applications in site map construction, Data and Knowledge Engineering, Vol. 43, Issue 2, Nov-2002

6. F. Masseglia, P. Poncelet, M. Teisseire, Incremental mining of sequential patterns in large databases, Data and Knowledge Engineering, Vol 46, Issue 1, Jul-2003

7. F. Alonso, J.P. Caraca-Valente, A.L. Gonzalez, C. Montes, Combining expert knowledge and data mining in a medical diagnosis, Expert Systems with Applications, Vol 23, Issue 4, Nov-2002

8. A. Conci, E.M.M.M. Castro, Image mining by content, Expert Systems with Applications, Vol 23, Issue 4, Nov-2002

9. D. Rosca, C. Wild, Towards a flexible deployment of business rules, Expert Systems with Applications, Vol 23, Issue 4, Nov-2002

10. M.A.F. de Souza, M.A.G.V. Ferreira, Designing reusable rule-based architectures with design patterns, Expert Systems with Applications, Vol 23, Issue 4, Nov-2002

11. .M. Chae, H.S. Kim, K.C. Tark, H.J. Park, S.H. Ho, Analysis of healthcare quality indicator using data mining and decision support system, Expert Systems with Applications, Volume 24, Issue 2, Feb-2003

12. R. Kruse, C. Borgelt, Information mining, International Journal of Approximate Reasoning, Volume 32, Issue 2-3, Feb-2003

13. T.A. Runkler, J.C. Bezdek, Web mining with relational clustering, International Journal of Approximate Reasoning, Volume 32, Issue 2-3, Feb-2003

14. G. Arevian, S. Wermter, C. Panchev, Symbolic state transducers and recurrent neural preference machines for text mining, International Journal of Approximate Reasoning, Volume 32, Issue 2-3, Feb-2003

15. S. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In Proceeding of the 1998 ACM 7th international conference on Information and knowledge management, pages 148-155, Washington United States, 1998

16. G. Hamerly and C. Elkan." Alternatives to the k-Means Algorithm That Find Better Clusterings (pdf),"Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM'02), November 2002.

17. Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, and Aynur A. Dayanik," Converting numerical classification into text classification," Artificial Intelligence, 143(1):51-77, January 2003.

18. F. Menczer," The Evolution of Document Networks," Proc. Natl. Acad. Sci. USA 101: 5261-5265, 2004.

19. Weiss, G. and F. Provost. "Learning when training data are costly: the effect of class distribution on tree induction," Journal of Artificial Intelligence Research 19 (2003), pp. 315-354.

20. Se-Hak Chun and Steven H. Kim, “Data mining for financial prediction and trading: application to single and multiple markets”, Expert System with Application, vol. 26, page(s): 131-139, 2004

21. J. furnkranz. “Exploiting structural information for text classification on the WWW.” In Proc. Of the 3rd Symposium on Intelligent Data Analysis (IDA), 1999

22. E. Glover, K. Tsioutsiouliklis, S. Lawrence, D. Pennock, and G. Flake. “Using web structure for classifying and describing web pages.” In Proc. of the WWW, 2002

23. Soumen Chakrabarti, Byron Dom, and Piotr Indyk. “Enhanced hypertext categorization using hyperlinks” ACM SIGMOD International Conference on Management of Data, 307-318,June 1998

24. A. Sun, E.P. Lim, W.K. Ng. “Web classification using support vector machine” WIDM, 2002

25. H.J. Oh, S.H. Myaeng, and M.H Lee. “A practical hypertext categorization method using links and incrementally available class information” In Proceedings of the Twenty Third ACM SIGIR Conference, 264-271, 2000

26. S. Slattery and Tom Mitchell. “ Discovering test set regularities in relational domains.” In Seventeenth International Conference on Machine Learning, June 2000

27. M. Craven and S. Slattery. “Relational learning with statistical predicate invention: Better models for hypertext.” Machine Learning, 43(1-2):97-119, 2001

28. P. Calado, Marco Cristo, E. S. de Moura , B. Ribeiro-Neto, and N. Ziviani. “Combining Link-Based and Content-Based Methods for Web Document Classification.” Conference on Information and Knowledge Management, 2003