Dissertation/ Thesis
Clustering for data analysis and privacy preservation in machine learning applications
| Title: | Clustering for data analysis and privacy preservation in machine learning applications |
|---|---|
| Authors: | Zhi, Yajing, 職亞婧 |
| Publisher Information: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Publication Year: | 2024 |
| Collection: | University of Hong Kong: HKU Scholars Hub |
| Subject Terms: | Data mining, Data privacy, Cluster analysis - Data processing, Machine learning |
| Description: | In this thesis, we explore the potential of integrating machine learning techniques, such as BERT, Transformer models, and federated learning, for predicting cryptocurrency price trends, specifically Bitcoin, and enabling collaborative and privacy-preserving deep learning for vision tasks. Analyzing almost five years of Reddit data, we investigate the Granger causality link between post volume, post sentiment, and Bitcoin price. Our findings demonstrate that post volume on Reddit better explains price trends than historical prices, emphasizing the importance of considering social media data in financial market predictions. We further evaluate the effectiveness of incorporating social media data and natural language processing methods, such as the Transformer model, in forecasting market trends. Apply different data clustering methods to process the original social media data in a rolling manner. We showcase the potential of the Transformer architecture in predicting Bitcoin price movements by carefully selecting appropriate clustering methods. Ad- Additionally, we explore the impact of incorporating outliers of social media data into the Transformer model to improve prediction accuracy. Given the privacy demand in modern applications like reducing the risk of exposing sensitive data, we further study privacy-preserving deep learning. We leverage the strengths of federated learning and Transformer models and apply the clustering techniques to address the challenge of privacy protection in large-scale deep learning. By combining these approaches, we can efficiently improve data privacy protection while enabling the application of powerful large-scale deep learning models. Our research demonstrates the potential of clustering methods on social media data, Transformer models for forecasting market trends, and clustering federated learning for privacy-preserving deep learning. Overall, our findings underscore the importance of integrating advanced machine learning techniques and social media data in predicting ... |
| Document Type: | doctoral or postdoctoral thesis |
| Language: | English |
| Relation: | HKU Theses Online (HKUTO); Zhi, Y. [職亞婧]. (2024). Clustering for data analysis and privacy preservation in machine learning applications. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.; 991044891406603414; https://hub.hku.hk/handle/10722/352639 |
| Availability: | https://hub.hku.hk/handle/10722/352639 |
| Rights: | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Accession Number: | edsbas.3CE36FEB |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hub.hku.hk/handle/10722/352639# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.3CE36FEB RelevancyScore: 872 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 871.7080078125 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Clustering for data analysis and privacy preservation in machine learning applications – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhi%2C+Yajing%22">Zhi, Yajing</searchLink><br /><searchLink fieldCode="AR" term="%22職亞婧%22">職亞婧</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: The University of Hong Kong (Pokfulam, Hong Kong) – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Hong Kong: HKU Scholars Hub – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Data+privacy%22">Data privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+-+Data+processing%22">Cluster analysis - Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: In this thesis, we explore the potential of integrating machine learning techniques, such as BERT, Transformer models, and federated learning, for predicting cryptocurrency price trends, specifically Bitcoin, and enabling collaborative and privacy-preserving deep learning for vision tasks. Analyzing almost five years of Reddit data, we investigate the Granger causality link between post volume, post sentiment, and Bitcoin price. Our findings demonstrate that post volume on Reddit better explains price trends than historical prices, emphasizing the importance of considering social media data in financial market predictions. We further evaluate the effectiveness of incorporating social media data and natural language processing methods, such as the Transformer model, in forecasting market trends. Apply different data clustering methods to process the original social media data in a rolling manner. We showcase the potential of the Transformer architecture in predicting Bitcoin price movements by carefully selecting appropriate clustering methods. Ad- Additionally, we explore the impact of incorporating outliers of social media data into the Transformer model to improve prediction accuracy. Given the privacy demand in modern applications like reducing the risk of exposing sensitive data, we further study privacy-preserving deep learning. We leverage the strengths of federated learning and Transformer models and apply the clustering techniques to address the challenge of privacy protection in large-scale deep learning. By combining these approaches, we can efficiently improve data privacy protection while enabling the application of powerful large-scale deep learning models. Our research demonstrates the potential of clustering methods on social media data, Transformer models for forecasting market trends, and clustering federated learning for privacy-preserving deep learning. Overall, our findings underscore the importance of integrating advanced machine learning techniques and social media data in predicting ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: doctoral or postdoctoral thesis – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: HKU Theses Online (HKUTO); Zhi, Y. [職亞婧]. (2024). Clustering for data analysis and privacy preservation in machine learning applications. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.; 991044891406603414; https://hub.hku.hk/handle/10722/352639 – Name: URL Label: Availability Group: URL Data: https://hub.hku.hk/handle/10722/352639 – Name: Copyright Label: Rights Group: Cpyrght Data: The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. – Name: AN Label: Accession Number Group: ID Data: edsbas.3CE36FEB |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.3CE36FEB |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Data mining Type: general – SubjectFull: Data privacy Type: general – SubjectFull: Cluster analysis - Data processing Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Clustering for data analysis and privacy preservation in machine learning applications Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhi, Yajing – PersonEntity: Name: NameFull: 職亞婧 IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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