A review of enhancing online learning using graph-based data mining techniques-Reference-Cited by-同舟云学术

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A review of enhancing online learning using graph-based data mining techniques

Published:2022-04-13 Issue:12 Volume:26 Page:5539-5552
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Munshi M.^ORCID,Shrimali Tarun,Gaur Sanjay

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-022-07034-7.pdf

Reference76 articles.

1. Ahmad S, Lavin A, Purdy S, Agha Z (2017) Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262:134–147. https://doi.org/10.1016/j.neucom.2017.04.070

2. Akoglu L, McGlohon M, Faloutsos C (2010) Oddball: spotting anomalies in weighted graphs. In: Pacific-Asia conference on knowledge discovery and data mining, Springer, Berlin. https://doi.org/10.1007/978-3-642-13672-6_40

3. Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: a survey. Data Min Knowl Discov 29(3):626–688. https://doi.org/10.1007/s10618-014-0365-y

4. Asghari A, Sohrabi MK, Yaghmaee F (2020) Online scheduling of dependent tasks of cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents. Soft Comput 24:16177–16199. https://doi.org/10.1007/s00500-020-04931-7

5. Barnard L, Lan WY, To YM, Paton VO, Lai SL (2009) Measuring self-regulation in online and blended learning environments. Internet High Educ 12(1):1–6. https://doi.org/10.1016/j.iheduc.2008.10.005

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