Database Reference
In-Depth Information
16. S. Brin, R. Motwani, and C. Silverstein. Beyond Market Baskets: Generalizing Association
Rules to Correlations.
ACM SIGMOD Conference
, 1997.
17. T. Calders, and B. Goethals. Mining all non-derivable frequent itemsets
Principles of Data
Mining and Knowledge Discovery
, pp. 1-42, 2002.
18. T. Calders, and B. Goethals. Depth-first Non-derivable Itemset Mining,
SDM Conference
,
2005.
19. T. Calders, N. Dexters, J. Gillis, and B. Goethals. Mining Frequent Itemsets in a Stream,
Informations Systems
, to appear, 2013.
20. J. H. Chang, and W. S. Lee. Finding Recent Frequent Itemsets Adaptively over Online Data
Streams,
ACM KDD Conference
, 2003.
21. M. Charikar, K. Chen, and M. Farach-Colton. Finding Frequent Items in Data Streams.
Automata, Languages and Programming
, pp. 693-703, 2002.
22. G. Cong, A. K. H. Tung, X. Xu, F. Pan, and J. Yang. FARMER: Finding interesting rule groups
in microarray datasets.
ACM SIGMOD Conference
, 2004.
23. G. Cong, K.-L. Tan, A. K. H. Tung, X. Xu. Mining Top-
k
covering Rule Groups for Gene
Expression Data.
ACM SIGMOD Conference
, 2005.
24. M. El-Hajj and O. Zaiane. COFI-tree Mining: A New Approach to Pattern Growth with Reduced
Candidacy Generation.
FIMI Workshop
, 2003.
25. F. Geerts, B. Goethals, J. Bussche. A Tight Upper Bound on the Number of Candidate Patterns,
ICDM Conference
, 2001.
26. B. Goethals. Survey on frequent pattern mining,
Technical report, University of Helsinki
, 2003.
27. R. P. Gopalan and Y. G. Sucahyo. High Performance Frequent Pattern Extraction using Com-
pressed FP-Trees,
Proceedings of SIAM International Workshop on High Performance and
Distributed Mining
, 2004.
28. K. Gouda, and M. Zaki. Genmax: An efficient algorithm for mining maximal frequent itemsets.
Data Mining and Knowledge Discovery
, 11(3), pp. 223-242, 2005.
29. G. Grahne, and J. Zhu. Efficiently Using Prefix-trees in Mining Frequent Itemsets,
IEEE ICDM
Workshop on Frequent Itemset Mining
, 2004.
30. G. Grahne, and J. Zhu. Fast Algorithms for Frequent Itemset Mining Using FP-Trees.
IEEE
Transactions on Knowledge and Data Engineering
. 17(10), pp. 1347-1362, 2005, vol. 17, no.
10, pp. 1347-1362, October, 2005.
31. V. Guralnik, and G. Karypis. Parallel tree-projection-based sequence mining algorithms.
Parallel Computing
, 30(4): pp. 443-472, April 2004.
32. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation,
ACM
SIGMOD Conference
, 2000.
33. 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.
34. C. Hidber. Online Association Rule Mining,
ACM SIGMOD Conference
, 1999.
35. R. Jin, and G. Agrawal. An Algorithm for in-core Frequent Itemset Mining on Streaming Data,
ICDM Conference
, 2005.
36. Q. Lan, D. Zhang, and B. Wu. A New Algorithm For Frequent Itemsets Mining Based On Apriori
And FP-Tree,
IEEE International Conference on Global Congress on Intelligent Systems
,
pp. 360-364, 2009.
37. D.-I. Lin, and Z. Kedem. Pincer-search: A New Algorithm for Discovering the Maximum
Frequent Set,
EDBT Conference
, 1998.
38. J. Liu, Y. Pan, K. Wang. Mining Frequent Item Sets by Opportunistic Projection,
ACM KDD
Conference
, 2002.
39. G. Liu, H. Lu and J. X. Yu. AFOPT:An Efficient Implementation of Pattern Growth Approach,
FIMI Workshop
, 2003.
40. H. Liu, J. Han, D. Xin, and Z. Shao. Mining frequent patterns on very high dimensional data:
a top- down row enumeration approach.
SDM Conference
, 2006.
41. C. Lucchesse, S. Orlando, and R. Perego. DCI-Closed: A fast and memory efficient algorithm
to mine frequent closed itemsets.
FIMI Workshop
, 2004.