Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77168
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dc.contributor.authorLu, Siyao
dc.date.accessioned2019-05-14T13:39:08Z
dc.date.available2019-05-14T13:39:08Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77168
dc.description.abstractSpectral clustering is currently a widely used method for community detection. This Final Year Project (FYP) researched and learned on spectral clustering comprehensively in three main perspectives. Firstly, a guideline on the selection of similarity matrices, adjacency matrix and Laplacian matrix, under different conditions is proposed through a large number of simulations in Chapter 2. Next, an improved spectral clustering method with more general input metrics is investigated with satisfying performance in Chapter 3. Lastly, two methods for the number of clusters K estimation are introduced and compared in Chapter 4. The better method is suggested based on simulation results under different number of nodes, clusters and blockmodels. Overall, the project overcomes the limitations of conventional spectral clustering algorithms towards similarity metrics and distance metrics, and constructs a complete flow to carry out spectral clustering.en_US
dc.format.extent47 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Science::Mathematicsen_US
dc.titleA comprehensive study on spectral clusteringen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorPan Guangmingen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.degreeBachelor of Science in Mathematical Sciencesen_US
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Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)
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