Database Reference
In-Depth Information
35. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu. FreeSpan: frequent
pattern-projected sequential pattern mining. ACM KDD Conference , 2000.
36. J. Han, J. Pei, H. Pinto, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu. PrefixSpan:
Mining sequential patterns efficiently by prefix-projected pattern growth. ICDE Conference ,
2001.
37. J. Han,
J.-G. Lee,
H. Gonzalez,
X. Li.
Mining Massive RFID, Trajectory,
and Traf-
fic Data Sets (Tutorial).
ACM KDD Conference ,
2008.
Video of Tutoral Lecture at:
http://videolectures.net/kdd08_han_mmrfid/
38. H. Jeung, M. L. Yiu, X. Zhou, C. Jensen, H. Shen, Discovery of Convoys in Trajectory
Databases, VLDB Conference , 2008.
39. R. Jin, G. Agrawal. Frequent Pattern Mining in Data Streams, Data Streams: Models and
Algorithms , pp. 61-84, Springer, 2007.
40. R. Jin, L. Liu, and C. Aggarwal. Discovering highly reliable subgraphs in uncertain graphs.
ACM KDD Conference , 2011.
41. G. Kuramuchi and G. Karypis. Frequent Subgraph Discovery, ICDM Conference , 2001.
42. A. R. Leach and V. J. Gillet. An Introduction to Chemoinformatics . Springer, 2003.
43. W. Lee, S. Stolfo, and P. Chan. Learning Patterns from Unix Execution Traces for Intrusion
Detection, AAAI workshop on AI methods in Fraud and Risk Management , 1997.
44. W. Lee, S. Stolfo, and K. Mok. A Data Mining Framework for Building Intrusion Detection
Models, IEEE Symposium on Security and Privacy , 1999.
45. J.-G. Lee, J. Han, K.-Y. Whang, Trajectory Clustering: A Partition-and-Group Framework,
ACM SIGMOD Conference , 2007.
46. J.-G. Lee, J. Han, X. Li. Trajectory Outlier Detection: A Partition-and-Detect Framework,
ICDE Conference , 2008.
47. J.-G. Lee, J. Han, X. Li, H. Gonzalez. TraClass: trajectory classification using hierarchical
region-based and trajectory-based clustering. PVLDB , 1(1): pp. 1081-1094, 2008.
48. X. Li, J. Han, and S. Kim. Motion-alert: Automatic Anomaly Detection in Massive Moving
Objects, IEEE Conference in Intelligence and Security Informatics , 2006.
49. X. Li, J. Han, S. Kim and H. Gonzalez. ROAM: Rule- and Motif-based Anomaly Detection in
Massive Moving Object Data Sets, SDM Conference , 2007.
50. Z. Li, B. Ding, J. Han, R. Kays. Swarm: Mining Relaxed Temporal Object Moving Clusters,
VLDB Conference , 2010.
51. C. Liu, X. Yan, H. Lu, J. Han, and P. S. Yu. Mining Behavior Graphs for “backtrace” of
non-crashing bugs, SDM Conference , 2005.
52. B. Liu, W. Hsu, Y. Ma. Integrating Classification and Association Rule Mining, ACM KDD
Conference , 1998.
53. S. Ma, and J. Hellerstein. Mining Partially Periodic Event Patterns with Unknown Periods,
IEEE International Conference on Data Engineering , 2001.
54. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering Frequent Episodes in Sequences,
ACM KDD Conference , 1995.
55. R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations
of constrained associations rules. ACM SIGMOD Conference , 1998.
56. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for
association rules. International Conference on Database Theory , pp. 398-416, 1999.
57. J. Pei, and J. Han. Can we push more constraints into frequent pattern mining? ACM KDD
Conference , 2000.
58. J. Pei, J. Han, R. Mao. CLOSET: An Efficient Algorithms for Mining Frequent Closed Itemsets,
DMKD Workshop , 2000.
59. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-mine: Hyper-structure mining of
frequent patterns in large databases. In Data Mining, ICDM Conference , 2001.
60. J. Pei, J. Han, and L. V. S. Lakshmanan. Mining Frequent Patterns with Convertible Constraints
in Large Databases, ICDE Conference , 2001.
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