A cognition graph approach for insights generation from event sequences-Reference-Cited by-同舟云学术

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A cognition graph approach for insights generation from event sequences

Published:2017-01-28 Issue:2 Volume:20 Page:1679-1690
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Wang Wei,Zhang Chen,Wang Hao,Gao Yang,Zheng Yuanman

Funder

National Natural Science Foundation of China (CN)

National Natural Science Foundation of China

Development Plan of Outstanding Young Talent from Institute of Software, Chinese Academy of Sciences

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

http://link.springer.com/article/10.1007/s10586-017-0744-4/fulltext.html

Reference54 articles.

1. Patnaik, D., Butler, P., Ramakrishnan, N., Parida, L., Keller, B.J., Hanauer, D.A.: Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 360–368. ACM (2011)

2. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1(3), 259–289 (1997)

3. Zhou, W., Liu, H., Cheng, H.: Mining closed episodes from event sequences efficiently. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 310–318. Springer, Berlin (2010)

4. Yan, X., Han, J.: Clospan: mining closed sequential patterns in large datasets. In: Proceedings of the Third SIAM International Conference on Data Mining, vol. 112, p. 166. SIAM (2003)

5. Cheng, H., Yan, X., Han, J.: Incspan: incremental mining of sequential patterns in large database. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 527–532. ACM (2004)

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