1.txt

This complete matlab for neural network

发信人: NAOMIELIE (雁来红), 信区: DataMining
标  题: Readings in Data Mining-Tentative Selection of In
发信站: 南京大学小百合站 (Wed Feb 19 10:13:22 2003)


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标  题: Readings in Data Mining  by老韩等
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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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