An efficient and scalable approach for mining subgraphs in a single large graph-Reference-Cited by-同舟云学术

An efficient and scalable approach for mining subgraphs in a single large graph

Published:2022-04-06 Issue:15 Volume:52 Page:17881-17895
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Nguyen Lam B. Q.,Nguyen Loan T. T.,Vo Bay^ORCID,Zelinka Ivan,Lin Jerry Chun-Wei,Yun Unil,Nguyen Hung Son

Funder

Ho Chi Minh City and the Department of Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03164-5.pdf

Reference42 articles.

1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. very large data bases, VLDB, vol 1215, pp 487-499

2. Han J, Pei J (2000) Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD Explorations Newsl 2(2):14–20

3. Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min Knowl Disc 8(1):53–87

4. Grahne G, Zhu J (2005) Fast algorithms for frequent itemset mining using fp-trees. IEEE Trans Knowl Data Eng 17(10):1347–1362

5. Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372–390

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