Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158048
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dc.contributor.authorYu, Shuaiqien_US
dc.date.accessioned2022-05-26T06:45:12Z-
dc.date.available2022-05-26T06:45:12Z-
dc.date.issued2022-
dc.identifier.citationYu, S. (2022). Clustering together with learning representations. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158048en_US
dc.identifier.urihttps://hdl.handle.net/10356/158048-
dc.description.abstractDocument clustering is a useful and practical machine learning methodology, with various real-world applications, such as search optimization, document recommendation, and tag generation of papers and records. It realizes the process of arranging a batch of pdf documents into many separate subgroups. To achieve more efficient clustering, we introduce representation learning, which is an unsupervised learning approach that self-studies the features from unlabeled data. In this project, we aim at implementing and studying a series of representation learning methods which are more suitable for clustering tasks on web documents such as Reuters-10k dataset. Specifically, the deep fuzzy clustering GrDNFCS has been implemented and explored to reproduce automatically categorize web documents reported in the paper. A new approach named CLDFC, where a contrastive loss is introduced into GrDNFCS is proposed and designed to improve accuracy of clustering. Based on our preliminary study, CLDEC shows 2.5% improvement in accuracy and reduce time complexity of average 60s per epoch compared with GrDNFCS. Experiments on several other clustering models will be included for comparisons.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleClustering together with learning representationsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLihui Chenen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailELHCHEN@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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