Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77168
Title: A comprehensive study on spectral clustering
Authors: Lu, Siyao
Keywords: DRNTU::Science::Mathematics
Issue Date: 2019
Abstract: 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.
URI: http://hdl.handle.net/10356/77168
Schools: School of Physical and Mathematical Sciences 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_Final_Report.pdf
  Restricted Access
1.25 MBAdobe PDFView/Open

Page view(s)

324
Updated on Jun 14, 2024

Download(s) 50

48
Updated on Jun 14, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.