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

In recent years, graph-based data mining (GDM) is the most accepted research due to numerous applications in a broad selection of software bug localization, computational biology, practical field, computer networking, and keyword searching. Moreover, graph data are subject to suspicions because of i...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 26; no. 12; pp. 5539 - 5552
Main Authors: Munshi, M., Shrimali, Tarun, Gaur, Sanjay
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2022
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Data Analytics and Machine Learning
Engineering
Mathematical Logic and Foundations
Mechatronics
Robotics
Cluster analysis
Data mining
Enhancing online learning
Graph-based data mining
Anomaly detection
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:In recent years, graph-based data mining (GDM) is the most accepted research due to numerous applications in a broad selection of software bug localization, computational biology, practical field, computer networking, and keyword searching. Moreover, graph data are subject to suspicions because of incompleteness and vagueness of data. Graph data mining of uncertain graphs is the most challenging and semantically different from correct data mining. The main problem of the GDM is mining uncertain graph data and subgraph pattern frequency. This paper discussed different techniques related to GDM, complexities, and the different size of the graph, and also investigated the dataset used for GDM, techniques of GDM like clustering analysis, and anomaly detection. To improve the performance of the online learning system, GDM is introduced. Additionally, the study algorithm is used for GDM, dataset, advantages, and disadvantages. In the end, future directions to enrich online learning based on the results of GDM are discussed. Performance metrics of different techniques such as accuracy, precision, recall, F-measure, and runtime are observed. Finally, conclude the survey with a discussion and overall performance of graph-based data mining.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-07034-7