Novel Approach to Minimize the Memory Requirements of Frequent Subgraph Mining Techniques
Author:
Affiliation:
1. Department of Computer EngineeringBursa Technical UniversityBursaTurkey
2. Microsoft CorporationTurkey BranchİstanbulTurkey
Publisher
Institution of Engineering and Technology (IET)
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/cje.2021.01.003
Reference30 articles.
1. A.Thusoo Z.Shao S.Anthony et al. “Data warehousing and analytics infrastructure at Facebook” ACM SIGMOD/PODS 2010 Conference Indianapolis Indiana USA pp.1013—1020 2010.
2. L.Wang Y.Xiao B.Shao et al. “How to partition a billionnode graph” 2014 IEEE International Conference on Data Engineering Chicago IL USA pp.568—579 2014
3. “Analysis of large graph partitioning and frequent subgraph mining on graph data”;Muttipati A.S.;International Journal of Advanced Research in Computer Science,2015
4. G.V.CarlosandM.Esteban “Comparative analysis of de Bruijn graph parallel genome assemblers” 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) IEEE pp.1—8 2018.
5. “A distributed approach for graph mining in massive networks”;Talukder N.;Data Mining and Knowledge Discovery,2016
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