WFSM-MaxPWS: An Efficient Approach for Mining Weighted Frequent Subgraphs from Edge-Weighted Graph Databases-Reference-Cited by-同舟云学术

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WFSM-MaxPWS: An Efficient Approach for Mining Weighted Frequent Subgraphs from Edge-Weighted Graph Databases

Published:2018 Issue: Volume: Page:664-676
ISSN:0302-9743
Container-title:Advances in Knowledge Discovery and Data Mining
language:
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Author:

Islam Md. Ashraful,Ahmed Chowdhury Farhan,Leung Carson K.^ORCID,Hoi Calvin S. H.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-93040-4_52

Reference17 articles.

1. Ahmed, C.F., Tanbeer, S.K., Jeong, B.S., Lee, Y.K., Choi, H.J.: Single-pass incremental and interactive mining for weighted frequent patterns. Expert Syst. Appl. 39(9), 7976–7994 (2012)

2. Babu, N., John, A.: A distributed approach to weighted frequent subgraph mining. In: ICETT 2016. IEEE (2016). https://doi.org/10.1109/ICETT.2016.7873705

3. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);Z Cheng,2017

4. Eichinger, F., Huber, M., Böhm, K.: On the usefulness of weight-based constraints in frequent subgraph mining. In: Bramer, M., Petridis, M., Hopgood, A. (eds.) Research and Development in Intelligent Systems, vol. XXVII, pp. 65–78. Springer, London (2011). https://doi.org/10.1007/978-0-85729-130-1_5

5. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);A Fariha,2013

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4. UGMINE: utility-based graph mining;Applied Intelligence;2022-04-12

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