A comprehensive study on spectral clustering

Spectral 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,...

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Main Author: Lu, Siyao
Other Authors: Pan Guangming
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/77168
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-771682023-02-28T23:18:54Z A comprehensive study on spectral clustering Lu, Siyao Pan Guangming School of Physical and Mathematical Sciences DRNTU::Science::Mathematics Spectral 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. Bachelor of Science in Mathematical Sciences 2019-05-14T13:39:08Z 2019-05-14T13:39:08Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77168 en 47 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Mathematics
spellingShingle DRNTU::Science::Mathematics
Lu, Siyao
A comprehensive study on spectral clustering
description Spectral 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.
author2 Pan Guangming
author_facet Pan Guangming
Lu, Siyao
format Final Year Project
author Lu, Siyao
author_sort Lu, Siyao
title A comprehensive study on spectral clustering
title_short A comprehensive study on spectral clustering
title_full A comprehensive study on spectral clustering
title_fullStr A comprehensive study on spectral clustering
title_full_unstemmed A comprehensive study on spectral clustering
title_sort comprehensive study on spectral clustering
publishDate 2019
url http://hdl.handle.net/10356/77168
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