A review of enhancing online learning using graph-based data mining techniques
Abstract 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 bec...
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| Autor*in: |
Munshi, M. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 26(2022), 12 vom: 13. Apr., Seite 5539-5552 |
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| Übergeordnetes Werk: |
volume:26 ; year:2022 ; number:12 ; day:13 ; month:04 ; pages:5539-5552 |
| Links: |
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| DOI / URN: |
10.1007/s00500-022-07034-7 |
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OLC2078735736 |
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| 520 | |a Abstract 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. | ||
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A review of enhancing online learning using graph-based data mining techniques |
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Abstract 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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract 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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract 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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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A review of enhancing online learning using graph-based data mining techniques |
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Shrimali, Tarun Gaur, Sanjay |
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