SEE UPCOMING SCHEUDULE HERE .
Association Rules
 Pradeep Shenoy, Jayant Haritsa, S. Sudarshan,Gaurav Bhalotia,
Mayank Bawa, Devavrat Shah: Turbocharging Vertical Mining of Large
Databases, SIGMOD'00. Will distribute when appropriate.

C. Aggarwal and P. Yu. Online generation of association rules. In IEEE International Conference on Data Engineering, February 1998.
The above paper is also available locally .

Quantifiable Data Mining Using Ratio Rules,
Flip Korn, Alexandros Labrinidis, Yannis Kotidis, Christos Faloutsos
The VLDB Journal 8(3+4), February 2000
The above paper is available locally .

Supervised and Unsupervised Discretization of Continuous Features
J. Dougherty, R. Kohavi and M. Sehrami, ICML 1995.
The above paper is available locally .

Automated Discovery of Active Motifs in Three Dimensional Molecules.
X. Wang et al. KDD 97.
The above paper is available locally .
The following papers can be downloaded from (you need to click on the data
mining link)http://www.cs.umn.edu/~kumar
 Interestingness Measures for Association Patterns :
A Perspective (2000). PangNing Tan and Vipin Kumar,
Technical Report # TR00036, U Minnesota.
The following papers can be downloaded from http://www.cs.sfu.ca/research/groups/DB/sections/publication/kdd/kdd.html
 J. Han, J. Pei, and Y. Yin, `` Mining Frequent Patterns without
Candidate Generation '', Proc. 2000 ACMSIGMOD Int. Conf. on
Management of Data (SIGMOD'00).
The following paper can be downloaded from
here

Shiby Thomas, Sreenath Bodagala, Khaled Alsabti, Sanjay Ranka. An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases. In Proceedings of the 3rd International conference on Knowledge Discovery and Data Mining (KDD 97), New Port Beach, California. August 1997.
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

R. Agrawal, T. Imielinski, A. Swami: ``Mining Associations between Sets
of Items in Massive Databases'', Proc. of the ACM SIGMOD Int'l Conference
on Management of Data, Washington D.C., May 1993, 207216.

R. Agrawal, R. Srikant: ``Fast Algorithms for Mining Association Rules'',
Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile,
Sept. 1994.

R. Bayardo, "Efficiently Mining Long Patterns from Databases", Proc. of
the ACM SIGMOD Conference on Management of Data, Seattle, Washington, June
1998.
The following papers can be downloaded from http://www.cc.gatech.edu/computing/Database/papers/pubs.html

A. Savasere , E. Omiecinski and S. Navathe. "An Efficient Algorithm for
Mining Association Rules in Large Databases ," In Proceedings of the Very
Large Data Base Conference, September, 1995.
The following papers can be downloaded from http://www.cs.Helsinki.FI/research/fdk/datamining

H. Toivonen. Sampling large databases for association rules. In 22th International
Conference on Very Large Databases (VLDB'96), 134145, Mumbay, India, September
1996.
The above paper is also available locally .
The following papers can be downloaded from http://www.cs.rpi.edu/~zaki/papers.html

Mohammed Javeed Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, Wei
Li, New Algorithms for Fast Discovery of Association Rules", 3rd International
Conference on Knowledge Discovery and Data Mining (KDD), pp 283286, Newport,
California, August, 1997 (Download Technical Report 651).
Association Rule Extensions
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

R. Srikant, R. Agrawal: "Mining Generalized Association Rules", Proc. of
the 21st Int'l Conference on Very Large Databases, Zurich, Switzerland,
Sep. 1995.

R. Srikant, R. Agrawal: "Mining Quantitative Association Rules in Large
Relational Tables", Proc. of the ACM SIGMOD Conference on Management of
Data, Montreal, Canada, June 1996.
The following papers can be downloaded from http://www.cs.sfu.ca/research/groups/DB/sections/publication/kdd/kdd.html

J. Han and Y. Fu, `` Discovery of MultipleLevel Association Rules from
Large Databases'', Proc. of 1995 Int'l Conf. on Very Large Data Bases (VLDB'95),
Zürich, Switzerland, September 1995, pp. 420431.

R. Ng, L. V. S. Lakshmanan, J. Han and A. Pang, `` Exploratory Mining
and Pruning Optimizations of Constrained Associations
Rules'', Proc. of 1998 ACMSIGMOD Conf. on Management of Data,
Seattle, Washington, June 1998.
Sequence Mining
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

R. Agrawal, R. Srikant: ``Mining Sequential Patterns'', Proc. of the Int'l
Conference on Data Engineering (ICDE), Taipei, Taiwan, March 1995.

R. Srikant, R. Agrawal: ``Mining Sequential Patterns: Generalizations and
Performance Improvements'', Proc. of the Fifth Int'l Conference on Extending
Database Technology (EDBT), Avignon, France, March 1996.
The following papers can be downloaded from http://www.cs.rpi.edu/~zaki/papers.html

Mohammed J. Zaki, Efficient Enumeration of Frequent Sequences, 7th International
Conference on Information and Knowledge Management, Washington DC, November
1998.

S. Parthasarathy, M. J. Zaki, M. Ogihara, S. Dwarkadas, Incremental
and Interactive Sequence Mining, 8th International
Conference on Information and Knowledge Management , Kansas City,
MO, November 1999.
The following papers can be downloaded from http://www.cs.Helsinki.FI/research/fdk/datamining

H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering Frequent Episodes
in Sequences. In First International Conference on Knowledge Discovery
and Data Mining (KDD'95), 210  215, Montreal, Canada, August 1995.

H. Mannila and H. Toivonen. Discovering generalized episodes using minimal
occurrences. In Second International Conference on Knowledge Discovery
and Data Mining (KDD'96), 146151, Portland, Oregon, August 1996.

H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of frequent
episodes in event sequences.
Data Mining and Knowledge Discovery, 1(3): 259  289, November
1997. (Preliminary Report
C199715, University of Helsinki, Department of Computer
Science, February 1997.)
The following papers can be downloaded from http://ekslwww.cs.umass.edu/pubs1.html

Oates, Tim, and Paul R. Cohen. 1996. Searching for Structure in Multiple
Streams of Data. In Proceedings of the Thirteenth International Conference
on Machine Learning, pp. 346354.
Classification
The following papers can be downloaded from
http://www.comp.nus.edu.sg/~dm2/publications.html

Bing Liu, Wynne Hsu, Yiming Ma, "Integrating Classification and Association Rule
Mining." Proceedings of the Fourth International Conference on Knowledge
Discovery and Data Mining (KDD98, Plenary Presentation), New York, USA, 1998.
The following papers can be downloaded from
http://www.cs.washington.edu/homes/pedrod/

Mining HighSpeed Data Streams, with Geoff Hulten. Proceedings of the
Sixth International Conference
on Knowledge Discovery and Data Mining (pp. 7180), 2000. Boston,
MA: ACM Press.
The following papers can be downloaded from
http://www.cs.cornell.edu/johannes/publications.html

J. E. Gehrke, Venkatesh Ganti, Raghu Ramakrishnan, and WeiYin
Loh. BOAT  Optimistic Decision Tree Construction. In
Proceedings of the 1999 SIGMOD Conference, Philadelphia,
Pennsylvania, 1999

Johannes Gehrke, Raghu Ramakrishnan, Venkatesh Ganti: RainForest  A Framework
for Fast Decision Tree Construction of Large Datasets. VLDB 1998: 416427
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

R. Agrawal and R. Srikant, "PrivacyPreserving Data Mining", Proc. of
the ACM SIGMOD Conference on
Management of Data, Dallas, May 2000.

J.C. Shafer, R. Agrawal, M. Mehta, "SPRINT: A Scalable Parallel Classifier
for Data Mining", Proc. of the 22th Int'l Conference on Very Large Databases,
Mumbai (Bombay), India, Sept. 1996

M. Mehta, R. Agrawal and J. Rissanen, ``SLIQ: A Fast Scalable
Classifier for Data Mining'', Proc. of the Fifth Int'l Conference on
Extending Database Technology, Avignon, France, March 1996.
The following papers can be downloaded from http://www.informatik.unitrier.de/~ley/db/conf/vldb/vldb96.html

Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama:
Constructing Efficient Decision Trees by Using Optimized Numeric Association
Rules. 146155
The following papers can be downloaded from http://citeseer.nj.nec.com/alsabti98clouds.html

K. Alsabti, S. Ranka and V. Singh. CLOUDS: A Decision Tree Classifer for
Large Datasets, Conference on Knowledge Discovery and Data Mining (KDD98)
( an extended version ).
The following papers can be downloaded from http://www.belllabs.com/user/rastogi/

R. Rastogi and K. Shim. PUBLIC: A decision tree classifier that integrates
building and pruning . In Proceedings of the Very Large Database Conference
(VLDB), New York, 1998
Clustering
The following papers can be downloaded from http://www.siam.org/meetings/sdm01/html/program.htm
Harsha Nagesh, Sanjay Goil, and Alok Choudhary,
Adaptive Grids for Clustering Massive Data Sets,
SIAM Conference on Data Mining, 2001.
The following papers can be downloaded from
http://www.acm.org/sigmod/dblp/db/conf/vldb/vldb99.html

Alexander Hinneburg, Daniel A. Keim:
Optimal GridClustering: Towards Breaking the Curse of
Dimensionality in HighDimensional Clustering. VLDB'99.
The following papers can be downloaded from
http://www.dbs.informatik.unimuenchen.de/dbs/project/publikationen/veroeffentlichungen_e.html/

Ester M., Kriegel H.P., Sander J., Wimmer M., Xu X.: Incremental
Clustering for Mining in a Data Warehousing Environment, 24th
Int. Conf. on Very Large Data Bases, New York, 1998, 323333.

Xu X., Ester M., Kriegel H.P., Sander J.: A DistributionBased
Clustering Algorithm for Mining in Large Spatial Databases, Proc. 14th
Int. Conf. on Data Engineering (ICDE'98), Orlando, FL, 1998,
pp. 324331.

Ester M., Kriegel H.P., Sander J., Xu X.: A DensityBased Algorithm
for Discovering Clusters in Large Spatial Databases with Noise,
Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD96),
Portland, OR, 1996, pp. 226231.
The following papers can be downloaded from http://www.belllabs.com/user/rastogi/

S. Guha, R. Rastogi and K. Shim. CURE: An efficient algorithm for clustering
large databases . In Proceedings of ACMSIGMOD 1998 International Conference
on Management of Data, Seattle, 1998.

S. Guha, R. Rastogi and K. Shim. ROCK: a robust clustering algorithm for
categorical attributes . In Proceedings of International Conference on
Data Engineering, 1999.
The following papers can be downloaded from http://www.research.microsoft.com/users/bradley/papers.html

P. S. Bradley, U. M. Fayyad and C. Reina. Scaling Clustering Algorithms
to Large Databases. Fourth International Conference on Knowledge Discovery
& Data Mining KDD98, pages 915. AAAI Press, Menlo Park, CA, 1998.
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan:
"Automatic Subspace Clustering of High Dimensional Data for Data Mining
Applications", Proc. of the ACM SIGMOD Int'l Conference on Management of
Data, Seattle, Washington, June 1998.
The following papers can be downloaded from http://www.cs.buffalo.edu/pub/WWW/DBGROUP/index.html

G. Sheikholeslami, S. Chatterjee, and A. Zhang, `` WaveCluster: A MultiResolution
Clustering Approach for Very Large Spatial Databases ,'' in the 24th International
Conference on Very Large Data Bases, August 2427, 1998, New York City.
The following papers can be downloaded from
http://www.cs.wisc.edu/~zhang/zhang.html

Tian Zhang, Raghu Ramakrishnan, Miron Livny: BIRCH: An Efficient Data Clustering
Method for Very Large Databases, SIGMOD96.
The above paper is also available locally .
The following papers can be downloaded from http://www.cs.sfu.ca/research/groups/DB/sections/publication/kdd/kdd.html

R. Ng and J. Han, `` Efficient and Effective Clustering Method for Spatial
Data Mining'', Proc. of 1994 Int'l Conf. on Very Large Data Bases (VLDB'94),
Santiago, Chile, September 1994,
The following papers can be downloaded from ftp://ftp.cs.umn.edu/dept/users/kumar/WEB/papers.html#bbbb

CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling
(1999). George Karypis, EuiHong (Sam) Han,
and Vipin Kumar, To appear in IEEE Computer: Special Issue on Data
Analysis and Mining.

Hypergraph Based Clustering in HighDimensional Data Sets: A Summary of
Results (1998). EuiHong (Sam) Han, George Karypis, Vipin Kumar and B.
Mobasher, Bulletin of the Technical Committee on Data Engineering, Vol.
21, No. 1, March 1998.
Database/Mining Integration
The following papers can be downloaded from
http://www.almaden.ibm.com/cs/quest/PUBS.html

S. Sarawagi, S. Thomas, R. Agrawal "Integrating association rule
mining with databases: alternatives and implications", Proc. of
the ACM SIGMOD Int'l Conference on Management of Data, Seattle,
Washington, June 1998.
The following papers can be downloaded from
http://research.microsoft.com/users/surajitc/
 Scalable Classification over SQL Databases, Proceedings of the
IEEE Data Engineering Conference, Sydney,
1999 (with Usama Fayyad and Jeff Bernhardt).
Deviation Detection
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

S. Sarawagi, R. Agrawal, N. Megiddo "Discoverydriven exploration of
OLAP data cubes", Proc. of the Sixth Int'l Conference on Extending
Database Technology (EDBT), Valencia, Spain, March 1998.

S. Chakrabarti, S. Sarawagi and B.Dom Mining surprising patterns using
temporal description length, Proc. of the 24th Int'l Conference on
Very Large Databases (VLDB), 1998. PDF format. Abstract.
The following papers can be downloaded from http://www.cs.ubc.ca:80/nest/dbsl/publications.html
 Edwin M. Knorr and Raymond T. Ng. "A Unified Notion of Outliers:
Properties and Computation", Proceedings of the 3rd International
Conference on Knowledge Discovery and Data Mining, Newport Beach, CA,
August 1417, 1997.
 Edwin M. Knorr and Raymond T. Ng. "Algorithms for Mining
DistanceBased Outliers in Large Datasets", Proceedings of the 24th
VLDB Conference, New York, August 2427, 1998.
Parallel Data Mining
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

R. Agrawal, J.C. Shafer, "Parallel Mining of Association Rules: Design,
Implementation and Experience", IBM Research Report RJ 10004, January 1996.

J.C. Shafer, R. Agrawal, M. Mehta, "SPRINT: A Scalable Parallel Classifier
for Data Mining", Proc. of the 22th Int'l Conference on Very Large Databases,
Mumbai (Bombay), India, Sept. 1996
The following papers can be downloaded from http://www.cs.rpi.edu/~zaki/papers.html

Mohammed Javeed Zaki, ChingTien Ho, Rakesh Agrawal, Scalable Parallel
Classification for Data Mining on SharedMemory Multiprocessors, IEEE International
Conference on Data Engineering, March 1999.
 Mohammed Javeed Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara,
Wei Li, New Parallel Algorithms for Fast
Discovery of Association Rules, Data Mining and Knowledge
Discovery: An International Journal, special issue on
Scalable HighPerformance Computing for KDD, pp 343373, Vol. 1,
No. 4, December 1997.

Mohammed Javeed Zaki, Srinivasan Parthasarathy, Wei Li,
A Localized Algorithm for Parallel Association Mining,
9th Annual ACM Symposium on Parallel Algorithms and Architectures (SPAA),
pp 321330, Newport, Rhode Island, June 2225, 1997.
The following papers can be downloaded from ftp://ftp.cs.umn.edu/dept/users/kumar/WEB/papers.html#bbbb

ScalParC: A New Scalable and Efficient Parallel Classification Algorithm
for Mining Large Datasets (1998). Mahesh V. Joshi, George Karypis and Vipin
Kumar, Proc. of 1998 International Parallel Processing Symposium, April
1998.

Scalable Parallel Data Mining for Association Rules(1997). EuiHong (Sam)
Han, George Karypis and Vipin Kumar, Proc. of 1997 ACMSIGMOD International
Conference on Management of Data, May 1997.
Web Mining
 Enhanced hypertext categorization using hyperlinks. With Byron
Dom and Piotr Indyk. In SIGMOD 1998. Available at
http://http.cs.berkeley.edu/~soumen/pub.html

Document Categorization and Query Generation on the World Wide Web
Using WebACE (1999). Daniel Boley, Maria Gini, Robert Gross, EuiHong
(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 WebPage Navigation, Cyrus
Shahabi, Amir Zarkesh, Jafar Abidi, and Vishal Shah, Seventh
International Workshop on Research Issues in Data Engineering, April
78,1997. Available from
http://imsc.usc.edu/Tools/profiler.html
 From User Access Patterns to Dynamic Hypertext Linking, Tak Woon Yan,
Matthew Jacobsen, Hector GarciaMolina, Umeshwar Dayal, Fifth
International World Wide Web Conference, May 1996. Avaialble online at
http://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. 1929. 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 WorldWideWeb. Proc. First PacificAsia Conference on
Knowledge Discovery and Data Mining (PAKDD97), Singapore,
February, 1997. Available from
http://www.csis.hku.hk/~dcheung/publication.html
 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
Spatial Data Mining
The following papers can be downloaded from http://www.cs.sfu.ca/research/groups/DB/sections/publication/kdd/kdd.html

K. Koperski, J. Han, and N. Stefanovic, `` An Efficient TwoStep
Method for Classification of Spatial Data'', Proc. 1998
International Symposium on Spatial Data Handling SDH'98, ,
Vancouver, BC, Canada, July 1998.
 K. Koperski, J. Adhikary and J. Han, `` Spatial Data Mining: Progress and
Challenges'', 1996 SIGMOD'96 Workshop. on Research Issues on Data Mining
and Knowledge Discovery (DMKD'96), Montreal, Canada, June 1996.
The following papers can be downloaded from http://www.cs.sfu.ca/research/groups/DB/sections/publication/smmdb/smmdb.html

K. Koperski and J. Han, `` Discovery of Spatial Association Rules in Geographic Information Databases'', Proc. 4th Int'l Symp. on Large Spatial
Databases (SSD95), Maine, Aug. 1995, pp. 4766.
 R. Ng and J. Han, `` Efficient and Effective Clustering Method for Spatial Data Mining'', Proc. of 1994 Int'l Conf. on Very Large Data Bases (VLDB'94),
Santiago, Chile, September 1994, pp. 144155.
The following papers can be downloaded from http://dml.cs.ucla.edu/publications/index.html

Wei Wang, Jiong Yang, Richard R. Muntz, STING: A Statistical Information
Grid Approach to Spatial Data Mining. VLDB97 , pp86195

"STING+: An Approach to Active Spatial Data Mining",
Wei Wang, Jiong Yang, Richard Muntz
International Conference on Data Engineering (ICDE99),
Australia, March, 1999.
The following papers can be downloaded from http://www.cs.ubc.ca:80/nest/dbsl/publications.html
 Edwin M. Knorr and Raymond T. Ng. "Finding Aggregate Proximity
Relationships and Commonalities in Spatial Data Mining", IEEE
Transactions on Knowledge and Data Engineering, Vol. 8, No. 6,
December, 1996, pp. 884897. Postscript
Similarity Search
The following papers can be downloaded from http://www.almaden.ibm.com/cs/quest/PUBS.html

R. Agrawal, K. Lin, H. S. Sawhney, K. Shim: ``Fast Similarity Search
in the Presence of Noise, Scaling, and Translation in
TimeSeries Databases'', Proc. of the 21st Int'l Conference on
Very Large Databases, Zurich, Switzerland, September 1995