Distributed frequent subgraph mining on evolving graph using SPARK-Reference-Cited by-同舟云学术

Distributed frequent subgraph mining on evolving graph using SPARK

Published:2020-05-21 Issue:3 Volume:24 Page:495-513
ISSN:1088-467X
Container-title:Intelligent Data Analysis
language:
Short-container-title:IDA

Author:

Senthilselvan N.¹,Subramaniyaswamy V.¹,Vijayakumar V.²,Karimi Hamid Reza³,Aswin N.¹,Ravi Logesh⁴

Affiliation:

1. School of Computing, SASTRA Deemed University, Thanjavur, India

2. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

3. Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy

4. Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science

Reference45 articles.

1. Incremental frequent subgraph mining on large evolving graphs;Abdelhamid;IEEE Transactions on Knowledge and Data Engineering,2017

2. A new algorithm for approximate pattern mining in multi-graph collections;Acosta-Mendoza;Knowledge-Based Systems,2016

3. A. Rakesh and R. Srikant, Fast algorithms for mining association rules, in: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, 1994.

4. B. Nisha and A. John, A distributed approach to weighted frequent Subgraph mining, Emerging Technological Trends (ICETT), in: International Conference on IEEE, 2016.

5. Ap-FSM: A parallel algorithm for approximate frequent subgraph mining using Pregel;Bhatia;Expert Systems with Applications,2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mining Association Rules from a Single Large Graph;Cybernetics and Systems;2023-01-18

2. Predictive analysis of real‐time strategy games: A graph mining approach;WIREs Data Mining and Knowledge Discovery;2020-12-08