Database Reference
In-Depth Information
13. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in
large databases.
ACM SIGMOD Conference
, 1993.
14. R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan. Automatic Subspace Clustering of High
Dimensional Data for Data Mining Applications,
ACM SIGMOD Conference
, 1998.
15. R. Agrawal, D. Gunopulos, and F. Leymann. Mining Process Models from Workflow Logs,
Springer
, 1998.
16. R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. A Tree Projection Algorithm for Generation
of Frequent Itemsets,
JPDC Journal
, 2001.
17. L. Akoglu, H. Tong, J. Vreeken, and C. Faloutsos. Fast and Reliable Anomaly Detection in
Categorical Data,
CIKM Conference
, 2012.
18. K. Ali, K. Manganaris, R. Srikant. Partial Classification using Association Rules,
KDD
Conference
, 1997.
19. P. Anick, and S. Tipirneni. The Paraphrase Search Assistant: Terminological Feedback for
Iterative Information Seekings,
ACM SIGIR
, 1999.
20. M.-L. Antonie, O. Zaiane, and A. Coman. Application of Data Mining Techniques for Medical
Image Classification,
Second International Workshop on Multimedia Data Mining at KDD
,
2001.
21. F. Beil, M. Ester, and X. Xu. Frequent Term-based Text Clustering,
ACM KDD Conference
,
2002.
22. M. Benkert, J. Gudmundsson, F. Hubner, and T. Wolle. Reporting flock patterns,
COMGEO
,
2008.
23. C. Bettini, X. S. Wang, S. Jajodia, and J. L. Lin. Discovering Frequent Event Patterns
with Multiple Granularities in Time Sequences,
IEEE Transactions on Knowledge and Data
Engineering
, 10(2), pp. 222-237, 1998.
24. H. Bohm and G. Schneider.
Virtual Screening for Bioactive Molecules
. Wiley-VCH, 2000.
25. C. Borgelt, M. Berthold. Mining molecular fragments: finding relevant substructures of
molecules.
ICDM Conference
, 2002.
26. G. Buehrer, and K. Chellapilla. A Scalable Pattern Mining Approach to Web Graph
Compression with Communities.
WSDM Conference
, 2009.
27. T. Calders, and B. Goethals. Mining all non-derivable frequent itemsets
Principles of Data
Mining and Knowledge Discovery
, pp. 1-42, 2002.
28. H. Cao, N. Mamoulis, D. W. Cheung. Mining Frequent Spatiotemporal Sequential Patterns,
ICDM Conference
, 2005.
29. W. Cavnar, and J. Trenkle. N-Gram based Text Categorization,
Proceedings of SDAIR
,
pp. 161-174, 1994.
30. 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.
31. C. Chen, X. Yan, F. Zhu, and J. Han. gapprox: Mining Frequent Approximate Patterns from
a Massive Network,
ICDM Conference
, 2007.
32. C. Cheng, A. Fu, Y. Zhang. Entropy-based Subspace Clustering for Mining Numerical Data,
ACM KDD Conference
, 1999.
33. H. Cheng, X. Yan, J. Han, and C.-W. Hsu. Discriminative Frequent Pattern Analysis for
Effective Classification,
ICDE Conference
, 2007.
34. G. Cong, A. Tung, X. Xu, F. Pan, and J. Yang. FARMER: Finding Interesting Rule Groups in
Microarray Data Sets,
ACM SIGMOD Conference
, 2004.
35. 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.
36. R. Cooley, B. Mobasher, and J. Srivasatava. Web mining: Information and pattern discovery
on the world wide web.
Ninth International Conference on Tools with Artificial Intelligence
,
1997.
37. R. Cooley, B. Mobaser, and J. Srivastava. Data preparation for mining world wide web
browsing patterns.
Knowledge and information systems
, 1(1), pp. 5-32, 1999.
38. B, Cule, N. Tatti, and B. Goethals. MARBLES: Mining Association Rules Buried in Long
Event Sequences.
SDM Conference
, 2002.
