Database Reference
In-Depth Information
10. R. Agrawal, T. Imielinski, and A. Swami. Database Mining: A Performance Perspective.
IEEE
Transactions on Knowledge and Data Engineering
, 5(6), pp. 914-925, 1993.
11. R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan. Automatic Subspace Clustering of High
Dimensional Data for Data Mining Applications,
ACM SIGMOD Conference
, 1998.
12. R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. Depth-first Generation of Long Patterns,
ACM KDD Conference
, 2000: Also appears as IBM Research Report, RC, 21538, 1999.
13. R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. A Tree Projection Algorithm for Generation of
Frequent Itemsets,
Journal of Parallel and Distributed Computing
, 61(3), pp. 350-371, 2001.
Also appears as IBM Research Report, RC 21341, 1999.
14. C. C. Aggarwal, N. Ta, J. Wang, J. Feng, M. Zaki. Xproj: A framework for projected structural
clustering of XML documents,
ACM KDD Conference
, 2007.
15. C. C. Aggarwal, Y. Li, J. Wang, J. Feng. Frequent Pattern Mining with Uncertain Data,
ACM
KDD Conference
, 2009.
16. C. Aggarwal, Y. Li, P. Yu, and R. Jin. On dense pattern mining in graph streams,
VLDB
Conference
, 2010.
17. R. J. Bayardo Jr. Efficiently mining long patterns from databases.
ACM SIGMOD Conference
,
1998.
18. J.-F. Boulicaut, A. Bykowski, and C. Rigotti. Free-sets: A Condensed Representation of
Boolean data for the Approximation of Frequency Queries.
Data Mining and Knowledge
Discovery
, 7(1), pp. 5-22, 2003.
19. G. Buehrer, and K. Chellapilla. A Scalable Pattern Mining Approach to Web Graph
Compression with Communities.
WSDM Conference
, 2009.
20. T. Calders,
and B. Goethals.
Mining all non-derivable frequent itemsets,
Principles of
Knowledge Discovery and Data Mining
, 2006.
21. T. Calders, C. Rigotti, and J. F. Boulicaut. A survey on condensed representations for frequent
sets. In
Constraint-based mining and inductive databases
, pp. 64-80, Springer, 2006.
22. J. H. Chang, W. S. Lee. Finding Recent Frequent Itemsets Adaptively over Online Data
Streams.
ACM KDD Conference
, 2003.
23. M. Charikar, K. Chen, and M. Farach-Colton. Finding Frequent Items in Data Streams,
Automata, Languages and Programming
, pp. 693-703, 2002.
24. M. S. Chen, J. S. Park, and P. S. Yu. Efficient data mining for path traversal patterns,
IEEE
Transactions on Knowledge and Data Engineering
, 10(2), pp. 209-221, 1998.
25. C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Zhu. Tools for privacy preserving
distributed data mining.
ACM SIGKDD Explorations Newsletter
, 4(2), pp. 28-34, 2002.
26. E. Cohen. M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J. Ullman, and C. Yang.
Finding Interesting Associations without Support Pruning,
IEEE TKDE
, 13(1), pp. 64-78,
2001.
27. G. Cormode, S. Muthukrishnan. What's hot and what's not: tracking most frequent items
dynamically,
ACM TODS
, 30(1), pp. 249-278, 2005.
28. J. Dean and S. Ghemawat.
MapReduce
: Simplified Data Processing on Large Clusters.
OSDI
,
pp. 137-150, 2004.
29. M. Deshpande, M. Kuramochi, N. Wale, and G. Karypis. Frequent substructure-based
approaches for classifying chemical compounds.
IEEE TKDE.
, 17(8), pp. 1036-1050, 2005.
30. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association
rules.
Information Systems
, 29(4), pp. 343-364, 2004.
31. M. Garofalakis, R. Rastogi, and K. Shim.: Sequential Pattern Mining with Regular Expression
Constraints,
VLDB Conference
, 1999.
32. V. Guralnik, and G. Karypis. Parallel tree-projection-based sequence mining algorithms.
Parallel Computing
, 30(4): pp. 443-472, April 2004.
33. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation,
ACM
SIGMOD Conference
, 2000.
34. J. Han, H. Cheng, D. Xin, and X. Yan. Frequent Pattern Mining: Current Status and Future
Directions,
Data Mining and Knowledge Discovery
, 15(1), pp. 55-86, 2007.
