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Michael Berry and Gordon Linoff, "Data Mining Techniques (For Marketing, Sales, and Customer Support), John Wiley & Sons, 1997.
Sholom M. Weiss and Nitin Indurkhya, "Predictive Data Mining: A Practical Guide", Morgan Kaufmann Publishers, 1998.
Alex Freitas and Simon Lavington, "Mining Very Large Databases with Parallel Processing", Kluwer Academic Publishers, 1998.
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General Data Mining: [10]
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Usama Fayyad, "Mining Databases: Towards Algorithms for Knowledge
Discovery", Bulletin of the IEEE Computer Society Technical Committee on
Data Engineering, vol. 21, no. 1, March 1998.
Christopher Matheus, Philip Chan, and Gregory Piatetsky-Shapiro, "Systems
for Knowledge Discovery in Databases", IEEE Transactions on Knowledge and
Data Engineering, 5(6):903-913, December 1993.
Rakesh Agrawal and Tomasz Imielinski, "Database Mining: A Performance
Perspective", IEEE Transactions on Knowledge and Data Engineering,
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Usama Fayyad, David Haussler, and Paul Stolorz, "Mining Scientific Data",
Communications of the ACM, vol. 39, no. 11, pp. 51-57, November 1996.
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Tom M. Mitchell, "Does machine learning really work?", AI Magazine, vol.
18, no. 3, pp. 11-20, Fall 1997.
Clark Glymour, David Madigan, Daryl Pregibon, and Padhraic Smyth,
"Statistical Inference and Data Mining", Communications of the ACM, vol.
39, no. 11, pp. 35-41, November 1996.
Hillol Kargupta, Ilker Hamzaoglu, and Brian Stafford, "Scalable,
Distributed Data Mining using An Agent Based Architecture", Proceedings of
the Third International Conference on Knowledge Discovery and Data Mining
(KDD-97), August 1997, Newport Beach, California, USA.
M-S. Chen, Jiawei Han, and Philip S. Yu, "Data Mining: An Overview from a
Database Perspective", IEEE Transactions on Knowledge and Data
Engineering, 8(6): 866-883, 1996.
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Intersection?", Bulletin of the IEEE Computer Society Technical Committee
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Classification: [20]
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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.
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.
Bing Liu and Wynne Hsu, "Post Analysis of Learned Rules", Proceedings of
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Aug 4-8, 1996, Portland, Oregon, USA, pp. 828-834.
Bing Liu, Wynne Hsu and Shu Chen, "Discovering Conforming and Unexpected
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Medicine and Pharmacology (IDAMAP-97), August 23-29, 1997, Nagoya, Japan
Bing Liu, Wynne Hsu and Shu Chen, "Using General Impressions to Analyze
Discovered Classification Rules", Proceedings of the Third International
Conference on Knowledge Discovery and Data Mining (KDD-97), pp. 31-36,
August 14-17, 1997, Newport Beach, California, USA.
K. Wang and B. Liu, "Concurrent Discretization of Multiple Attributes",
PRICAI 98, August 1998, Singapore
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Association Rules: [24]
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R. Agrawal, T. Imielinski, A. Swami, "Mining Associations between Sets of
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Discovery of Association Rules", Advances in Knowledge Discovery and Data
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Proc. of the 20th Int'l Conference on Very Large Databases, Santiago,
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Constraints", Proc. of the 3rd Int'l Conference on Knowledge Discovery in
Databases and Data Mining, Newport Beach, California, August 1997.
R. Srikant, R. Agrawal: "Mining Generalized Association Rules", Proc. of
the 21st Int'l Conference on Very Large Databases, Zurich, Switzerland,
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Charu Aggarwal and Philip Yu, "Mining Large Itemsets for Association
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ACM-SIGMOD Conf. on Management of Data, Seattle, Washington, June 1998.
Sergey Brin, Rajeev Motwani, Craig Silverstein, "Beyond Market Baskets:
Generalizing Association Rules to Correlations", Proceedings of 1997 ACM
SIGMOD, Montreal, Canada, June 1997.
Sergey Brin, Rajeev Motwani, Dick Tsur, Jeffrey Ullman, "Dynamic Itemset
Counting and Implication Rules for Market Basket Data", Proceedings of
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Dick Tsur, Jeffrey Ullman, Chris Clifton, Serge Abiteboul, Rajeev Motwani,
Svetlozar Nestorov, Arnie Rosenthal, "Query Flocks: a Generalization of
Association-Rule Mining", Proceedings of 1998 ACM SIGMOD, Seattle,
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Optimized Rectilinear Regions for Association Rules", Proceedings of the
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Numeric Association Rules" , Proc. International Conference on Knowledge
Discovery and Data Mining (KDD-98), August 1998, New York City.
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Estimating Quantiles for Disk-Resident Data", In Proc. of VLDB'97
R. Agrawal and J. C. Shafer, "Parallel Mining of Association Rules:
Design, Implementation and Experience", IEEE Transactions on Knowledge
Data and Engg., 8(6):962-969, December 1996.
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Data Mining for Association Rules", Proc. of 1997 ACM-SIGMOD International
Conference on Management of Data, May 1997.
T. Shintani and M. Kitsuregawa, "Hash Based Parallel Algorithm for
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H. Mannila and H. Toivonen, "Multiple uses of frequent sets and condensed
representations", Proc. Second International Conference on Knowledge
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1996.
H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hdtvnen, and H. Mannila,
"Pruning and grouping discovered association rules", In MLnet Workshop on
Statistics, Machine Learning, and Discovery in Databases, pp. 47-52,
Heraklion, Crete, Greece, April 1995.
Jose Borges and Mark Levene, "Mining Association Rules in Hypertext
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Mining (KDD-98), August 1998, New York City.
Mohammed J. Zaki and Mitsunori Ogihara, "Theoretical Foundations of Association
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Knowledge Discovery (DMKD), Seattle, WA, June 1998.
Clustering: [17]
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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. 144-155.
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Spatial Databases: The Algorithm GDBSCAN and its Applications", Data Mining
and Knowledge Discovery, An International Journal, Kluwer Academic
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Tian Zhang, Raghu Ramakrishnan, Miron Livny, "BIRCH: An Efficient Data
Clustering Method for Very Large Databases", Proc. of ACM SIGMOD Int'l
Conf. on Data Management, June 1996, Canada.
Peter Cheeseman and John Stutz, "Bayesian Classification (AutoClass):
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R. Uthurusamy (eds.), "Advances in Knowledge Discovery and Data Mining",
pp 153-180, AAAI/MIT Press, 1996.
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.
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Datasets", Proc. International Conference on Knowledge Discovery and Data
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and Data Mining (KDD-98), August 1998, New York City.
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of Web Documents", Proc. of the Third International Conference on Knowledge
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G. D. Ramkumar and A. Swami, "Clustering Data Without Distrance Functions",
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Data Engineering, Australia, 1999.
Venkatesh Ganti, Raghu Ramakrishnan, and Johannes Gehrke, "Clustering
Large Datasets in Arbitrary Metric Spaces", Proceedings of IEEE Conference
on Data Engineerings, Austratlia, 1999.
F. Chen, S. Figlewski, J. Heisler, and A. S. Weigend, "Uncovering Hidden
Structure in Bond Futures Trading", Proc. of IEEE?IAFE/INFORMS Conference
on Computational Intelligence for Financial Engineering (CIFEr'98),
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Padhraic Smyth, "Clustering Sequences with Hidden Markov Models", in
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Mining Sequential (or Temporal) Data: [25]
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Philip Laird, "Identifying and Using Patterns in Sequential Data", in
Algorithmic Learning Theory (ALT'93), Fourth International Workshop,
pp. 1-18, Springer-Verlag.
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G. Berger and A. Tuzhilin, "Discovering Unexpected Patterns in Temporal Data
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pp. 283-294, Melbourne, Australia, April 1998.
M. Joshi, G. Karypis, and V. Kumar, "Parallel Algorithms for
Mining Sequential Associations", Presented at the Minisymposium on High
Performance Data Mining at Ninth SIAM Conference on Parallel Processing
for Scientific Computing, San Antonio, Texas, March 1999.
Mohammed Zaki, "Efficient Enumeration of Frequent Sequences", 7th
International Conference on Information and Knowledge Management,
Washington DC, November 1998.
Ke Wang, "Discovering Patterns from Large and Dynamic Sequential Data",
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V. Guralnik, D. Wijesekera, and J. Srivastava, "Pattern Directed Mining
for Frequent Episodes", Proc. of the Fourth International Conference
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K. Hatonen, M. Klemettinen, H. Mannila, P. Ronkainen, and H. Toivonen, "TASA:
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network", In IEEE/IFIP 1996 Network Operations and Management Symposium
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E. Arjas, H. Mannila, M. Salmenkivi, R. Suramo, and H. Toivonen, "BASS:
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Sequences", Proc. of the Fourth International Conference on Knowledge
Discovery and Data Mining (KDD-98), New York, USA, 1998.
B. Lent, R. Agrawal, R. Srikant, "Discovering Trends in Text Databases",
Proc. of the 3rd Int'l Conference on Knowledge Discovery in Databases and
Data Mining (KDD'97), Newport Beach, California, August 1997.
J. Han, W. Gong, and Y. Yin, "Mining Segment-Wise Periodic Patterns in
Time-Related Databases", Proc. of 1998 Int'l Conf. on Knowledge Discovery
and Data Mining (KDD'98) , New York City, NY, Aug. 1998.
T. H. Hinke, J. Rushing, H. Ranganath, and S. J. Graves, "Target-Independent
Mining For Scientific Data: Capturing Transients and Trends for Phenomena
Mining", Proc. of the 3rd Int'l Conference on Knowledge Discovery in
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Honghua Dai, "Trend Directed Learning: A Case Study", in Research and
Development in Knowledge Discovery and Data Mining: Second Pacific-Asia
Conference (PAKDD'98), pp. 283-294, Melbourne, Australia, April 1998.
K.-R. Muller, A. J. Smola, G. Ratsch, B. Scholkopf, J. Kohlmorgen, and V.
Vapnik, "Predicting Time Series with Support Vector Machines", Proceedings
ICANN'97, Springer Lecture Notes in Computer Science, 1997.
R. Bharat Rao, S. Rickard, and F. Coetzee, "Time Series Forecasting from
High-Dimensional Data with Multiple Adaptive Layers", Proc. of 1998 Int'l
Conf. on Knowledge Discovery and Data Mining (KDD'98) , New York City,
NY, Aug. 1998.
E. J. Keogh and M. J. Pazzani, "An enhanced representation of time series
which allows fast and accurate classification, clustering, and relevance
feedback", AAAI Workshop on Predicting the Future: AI Approaches to Time
Series Analysis. Madison, Wisc., 1998.
S.-P. Shieh and V. D. Gligor, "On a Pattern-Oriented Model for Intrusion
Detection", IEEE Trans. on Knowledge and Data Engineering, vol. 9, no. 4,
July/August 1997, pp. 661-667.
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.
Searching More Papers by Author,Title, etc.
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http://www.cs.Helsinki.FI/research/pmdm/datamining/datamine.html]
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http://www.almaden.ibm.com/cs/quest/publications.html]
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http://www.almaden.ibm.com/cs/quest/publications.html]
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http://www.isse.gmu.edu/~jlin/tdb]
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http://www.tkl.iis.u-tokyo.ac.jp/Kilab/Research/Paper/paper1998.html]
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http://www.cs.rpi.edu/~zaki/papers.htm]
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http://www.comp.nus.edu.sg/~wangk/publication.html]
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http://www.cs.Helsinki.FI/research/pmdm/datamining/datamine.html]
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http://www.cs.Helsinki.FI/research/pmdm/datamining/datamine.html]
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http://www.almaden.ibm.com/cs/quest/publications.html]
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http://db.cs.sfu.ca/sections/publication/kdd/kdd.html]
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http://svm.first.gmd.de/publications.html]
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http://www.ics.uci.edu/~mlearn/MLPapers.html]
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http://www.almaden.ibm.com/cs/quest/publications.html]
Prepared By Mahesh Joshi. Please
mail any additions/corrections.