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Readings in Data Mining-Tentative Selection of Influential Paper 


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Introduction 


Iintroduction by co-editors. 


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Data Warehouse and OLAP Technology for Data Mining


J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. 


Pellow, and H. Pirahesh. Data cube: A relational aggregation operator 

generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge 

Discovery, 1(1):29-54, 1997. 


(??) V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data 

cubes efficiently.  In SIGMOD'96, pp. 205-216, Montreal, Canada, June 1996. 



S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. 

Ramakrishnan, and S. Sarawagi. On the computation of multidimensional 

aggregates. In Proc. 1996 Int. Conf. Very Large Data Bases (VLDB'96), pp. 

506-521, Bombay, India, Sept. 1996.   


Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-based algorithm for 

simultaneous multidimensional aggregates. In SIGMOD'97, pp. 159-170, 

Tucson, Arizona, May 1997. 


K. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and iceberg 

cubes. In SIGMOD'99, pp. 359--370, Philadelphia, PA, June 1999. 


J. Han, J. Pei, G. Dong, and K. Wang. Efficient computation of iceberg 

cubes with complex measures. In SIGMOD'01, pp. 1--12, Santa Barbara, CA, 

May 2001. 


W. Wang, H. Lu, J. Feng, and J. X. Yu. Condensed Cube: An Effective 

Approach to Reducing Data Cube Size. In Proc. 2002 Int. Conf. Data 

Engineering (ICDE'02) , San Fransisco, CA, April 2002. 

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Data Preprocessing 


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. 


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Data Cube Exploration and Concept Description 


J. Han, Y. Cai and N. Cercone, Knowledge Discovery in Databases: An 

Attribute-Oriented Approach in (VLDB'92) , Vancouver, Canada, August 1992, 

pp. 547-559. 


[??] K. A. Ross, D. Srivastava, and D. Chatziantoniou. Complex aggregation 

at multiple granularities. In EDBT'98, pp. 263-277, Valencia, Spain, March 

1998. 


S. Sarawagi, R. Agrawal, and N. Megiddo. Discovery-driven exploration of 

OLAP data cubes. In Proc. Int. Conf. of Extending Database Technology 

(EDBT'98), Valencia, Spain, pp. 168-182, March 1998 


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Mining Association Rules in Large Databases 


R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In 


VLDB'94, pp. 487-499, Santiago, Chile, Sept. 1994. 


H. Mannila and H Toivonen. Level-wise search and borders of theories in 

knowledge discovery. Data Mining and Knowledge Discovery, 1, 1997. 


R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and 

pruning optimizations of constrained associations rules. In SIGMOD'98, pp. 

13-24 Seattle, Washington, June 1998. 


N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent 

closed itemsets for association rules. In Proc. 7th Int. Conf. Database 

Theory (ICDT'99), pages 398-416, Jerusalem, Israel, Jan. 1999. 


R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In Proc. 

1999 Int. Conf. Knowledge Discovery and Data Mining (KDD'99), pp. 145-154, 

San Diego, CA, Aug. 1999. 


R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm 

for generation of frequent itemsets. In Journal of Parallel and Distributed 


Computing, 2000. 


J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate 

Generation., Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data 

(SIGMOD'00), Dallas, TX, May 2000. 


M. J. Zaki and C. J. Hsiao. CHARM: An efficient algorithm for closed 

itemset mining.  In Proc. 2002 SIAM Int. Conf. Data Mining, Arlington, VA, 

April 2002. 


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Classification and Prediction 


J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 

1986. 


any representative papers on CART? 


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. 


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. 


M. Ankerst, M. Ester, and H.-P. Kriegel. Towards an effective cooperation 

of the user and the computer for classification. In Proc. 2000 Int. Conf. 

Knowledge Discovery and Data Mining (KDD'00), pages 179-188, Boston, MA, 

Aug. 2000. 


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Cluster Analysis 


R. Ng and J. Han. Efficient and effective clustering method for spatial 

data mining. In VLDB'94, pp. 144-155, Santiago, Chile, Sept. 1994. 


T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data 

clustering method for very large databases. In SIGMOD'96, pp. 103-114, 

Montreal, Canada, June 1996. 


M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm 

for discovering clusters in large spatial databases. In KDD'96, pp. 

226-231, Portland, Oregon, August 1996. 


R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace 

clustering of high dimensional data for data mining applications. In 

SIGMOD'98, pp. 94-105, Seattle, Washington, June 1998. 


M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. Optics: Ordering 

points to identify the clustering structure. In SIGMOD'99, pp. 49-60, 

Philadelphia, PA, June 1999. 


E. Knorr and R. Ng. Algorithms for mining distance-based outliers in large 

datasets. In Proc. 1998 Int. Conf. Very Large Data Bases (VLDB'98), pages 

392-403, New York, NY, Aug. 1998. 


M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander. LOF: Identifying 

Density-Based Local Outliers. In Proc. ACM SIGMOD Int. Conf. on Management 

of Data (SIGMOD 2000), Dallas, TX, 2000, pp. 93-104. 


G. Karypis, E.-H. Han, and V. Kumar. CHAMELEON: A Hierarchical Clustering 

Algorithm Using Dynamic Modeling. COMPUTER, 32(8): 68-75, 1999. 


Wei Wang, Jiong Yang, Richard Muntz. STING+: an approach to active spatial 

data mining (ICDE 99, pp. 116-125) 


S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering Data 

Streams, Proc. IEEE Symposium on Foundations of Computer Science (FOCS'00), 


Redondo Beach, CA, pp. 359-366, 2000 


C. Aggarwal, and P.S. Yu, Re-defining Clustering for High Dimensional 

Applications, IEEE Trans. Knowledge and Data Eng., Vol. 14, No.2, March 

2002, pp. 210-225. 


H. Wang, W. Wang, J. Yang, and P.S. Yu.  Clustering by pattern similarity 

in large data sets,  Proc. the ACM SIGMOD International Conference on 

Management of Data (SIGMOD), Madison, Wisconsin, 2002. 


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Mining Trends and Sequential Patterns in Time-Series or Sequence databases 


R. Agrawal, C. Faloutsos, and A. Swami. Efficient similarity search in 

sequence databases. In Proc. 4th Int. Conf. Foundations of Data 

Organization and Algorithms, pp. 69-84, Chicago, IL, Oct. 1993. 


R. Agrawal, G. Psaila, E. L. Wimmers, and M. Zait. Querying shapes of 

histories. In VLDB'95, pp. 502-514, Zürich, Switzerland, Sept. 1995. 


R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and 

performance improvements. In Proc. 5th Int. Conf. Extending Database 

Technology (EDBT'96), pages 3-17, Avignon, France, Mar. 1996. 


Mannila H.; Toivonen H.; Inkeri Verkamo A., Discovery of Frequent Episodes 

in Event Sequences. Data Mining and Knowledge Discovery, 1997, vol. 1, no. 

3, pp. 259-289(31) 


M. Garofalakis, R. Rastogi, and K. Shim. SPIRIT: Sequential pattern mining 

with regular expression constraints. In Proc. 1999 Int. Conf. Very Large 

Data Bases (VLDB'99), pp. 223-234, Edinburgh, UK, Sept. 1999. 


J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: 

Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. 

, Proc. 2001 Int. Conf. on Data Engineering (ICDE'01), Heidelberg, Germany, 


April 2001. 


J. Yang, P. Yu, W. Wang, and J. Han, '' Mining Long Sequential Patterns in 

a Noisy Environment '', Proc. 2002 ACM-SIGMOD Int. Conf. on Management of 

Data (SIGMOD'02)}, Madison, WI, June 2002. 


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Web Mining 


S. Chakrabarti, B. E. Dom, D. Gibson, J. M. Kleinberg, P. Raghavan, and S. 

Rajagopalan. Automatic resource compilation by analyzing hyperlink 

structure and associated text. In Proc. 7th Int. World Wide Web Conf. 

(WWW'98), pp. 65-74, Brisbane, Australia, 1998. 


J. M. Kleinberg. Authoritative Sources in a Hyperlinked Environment. 

Journal of ACM, 46(5):604-632, 1999. 


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Data Mining Applications and Social Impacts of Data Mining 


H. V. Jagadish, J. Madar, and R. Ng. Semantic compression and pattern 

extraction with fascicles. In Proc. 1999 Int. Conf. Very Large Data Bases 

(VLDB'99), pages 186-197, Edinburgh, UK, Sept. 1999. 


R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proc. 2000 

ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'00), pages 439-450, 

Dallas, TX, May 2000. 

 

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