Links to Papers

Data Mining


Introductory Material	S. Chaudhuri and U. Dayal: An Overview of Data Warehousing and OLAP Technology. SIGMOD Record 26(1): 65-74 (1997). M.-S. Chen, J. Han and P. S. Yu. Data mining: an overview from a database perspective. IEEE Trans. On Knowledge And Data Engineering, 8:866-883, 1996. U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth. From data mining to knowledge discovery: an overview. In U.M. Fayyad et al. ed. Advances in Knowledge Discovery and Data Mining, AAAI/MIT, 1996, 1-34. A. Silberschatz, M. Stonebraker and J. D. Ullman. Database research: achievements and opportunities into the 21st century.SIGMOD Record, 25(1):52-63, March 1996. V. Ganti, J. Gehrke and R. Ramakrishnan. Mining very large databases. COMPUTER, 32(8):38-45, 1999. S. Chaudhuri, U. Dayal and V. Ganti, Database Technology for Decision Support Systems. Computer, 34(12):48-55, Dec. 2001.
Data Cleaning	Real-World Data is Dirty: Data Cleansing and The Merge/Purge Problem. M. Hernandez and S. Stolfo, Journal of Data Mining and Knowledge Discovery, 1997. http://www.cs.columbia.edu/~sal/hpapers/mp.ps Declaratively cleaning your data using AJAX. Helena Galhardas, Daniela Florescu, Dennis Shasha and Eric Simon. http://citeseer.nj.nec.com/309494.html An adaptive and efficient algorithm for detecting approximately duplicate database records. Alvaro E. Monge. A Framework for Research in Data Cleaning. Dallan Quass. http://caravel.inria.fr/~galharda/quass.cleaningII.ps V. Raman and J. M. Hellerstein. Potter's Wheel: An Interactive Data Cleaning System, Proc. 2001 Int. Conf. on Very Large Data Bases (VLDB'01), Rome, Italy, pp. 381-390, Sept. 2001. D. Barbará et al. The New Jersey Data Reduction Report. Bulletin of the Technical Committee on Data Engineering, 20, Dec. 1997, pp. 3-45. Data Integration. UCLA CSE554 Lecture notes A. Maydanchik, Challenges of Efficient Data Cleansing (DM Review - Data Quality resource portal) E. Rahm and H. H. Do. Data Cleaning: Problems and Current Approaches. IEEE Bulletin of the Technical Committee on Data Engineering. Vol.23, No.4, 2000.
Classification	J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986. S. M. Weiss and N. Indurkhya. Predictive Data Mining. Morgan Kaufmann, 1997. T. M. Mitchell. Machine Learning. McGraw Hill, 1997. J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. In VLDB'96, pp. 544-555, Bombay, India, Sept. 1996. J. Gehrke, R. Ramakrishnan, V. Ganti. RainForest: A framework for fast decision tree construction of large datasets. In VLDB'98, pp. 416-427, New York, NY, August 1998. J. Gehrke, V. Gant, R. Ramakrishnan, and W.-Y. Loh, BOAT -- Optimistic Decision Tree Construction . In SIGMOD'99 , Philadelphia, Pennsylvania, 1999 S. K. Murthy. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery, 2(4): 345-389, 1998. C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2(2): 121-168, 1998. B. Liu, W. Hsu, and Y. Ma. Integrating Classification and Association Rule Mining. Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD'98) New York, NY, Aug. 1998. W. Li, J. Han, and J. Pei, CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules, , Proc. 2001 Int. Conf. on Data Mining (ICDM'01), San Jose, CA, Nov. 2001.
Gini Index	Further Data Mining: Building Decision Trees, Nathan Rountree. (some explaination for Gini Index).
Web Mining * (Links from Prof.Zaki's page at RPI)	Web Mining: Information and Pattern Discovery on the World Wide Web (A Survey Paper) (1997) (with R. Cooley and J. Srivastava), in Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'97), November 1997. Available from http://maya.cs.depaul.edu/~mobasher/pubs.html Enhanced hypertext categorization using hyperlinks. With Byron Dom and Piotr Indyk. In SIGMOD 1998. Available athttp://http.cs.berkeley.edu/~soumen/ Document Categorization and Query Generation on the World Wide Web Using WebACE (1999). Daniel Boley, Maria Gini, Robert Gross, Eui-Hong (Sam) Han, Kyle Hastings, George Karypis, Vipin Kumar, Bamshad Mobasher, and Jerome Moore, To appear in AI Review. Available from ftp://ftp.cs.umn.edu/dept/users/kumar/WEB/papers.html#bbbb Knowlege Discovery from User's Web-Page Navigation, Cyrus Shahabi, Amir Zarkesh, Jafar Abidi, and Vishal Shah, Seventh International Workshop on Research Issues in Data Engineering, April 7-8,1997. Available from http://imsc.usc.edu/Tools/profiler.html From User Access Patterns to Dynamic Hypertext Linking, Tak Woon Yan, Matthew Jacobsen, Hector Garcia-Molina, Umeshwar Dayal, Fifth International World Wide Web Conference, May 1996. Avaialble online athttp://www5conf.inria.fr/fich_html/papers/P8/Overview.html. O. R. Zaiane, M. Xin, J. Han, `` Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs'', Proc. Advances in Digital Libraries Conf. (ADL'98), Santa Barbara, CA, April 1998, pp. 19-29. Avaialble at http://www.cs.sfu.ca/research/groups/DB/sections/publication/smmdb/smmdb.html D.W. Cheung, B. Kao, and J.W. Lee, Discovering User Access Patterns on the World-Wide-Web. Proc. First Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-97), Singapore, February, 1997. Available from http://www.csis.hku.hk/~dcheung/publication.html Soumen Chakrabarti, Byron Dom, Rakesh Agrawal, Prabhakar Raghavan: Scalable Feature Selection, Classification and Signature Generation for Organizing Large Text Databases into Hierarchical Topic Taxonomies, VLDB Journal 1998. Available at http://www.cs.berkeley.edu/~soumen/VLDB54_3.PDF
Text Mining
Clustering	R. Ng and J. Han, Efficient and Effective Clustering Method for Spatial Data Mining,VLDB’94 T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH : an efficient data clustering method for very large databases. SIGMOD'96. G. Karypis, Eui-Hong Han, V. Kumar. "CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. " In IEEE Computer, Aug. 1999. W. Wang, J. Yang, and R. Muntz. STING: a statistical information grid approach to spatial data mining. VLDB’97 M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. Optics: Ordering points to identify the clustering structure, SIGMOD’99. Edwin M. Knorr, Raymond T. Ng, and V. Tucakov. Distance-Based Outliers: Algorithms and Applications. VLDB Journal, 8(3-4):237--253, 2000.