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Please use this identifier to cite or link to this item: http://dspace.cityu.edu.hk/handle/2031/9502
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dc.contributor.authorWong, Ho Sumen_US
dc.date.accessioned2021-11-17T04:08:45Z-
dc.date.available2021-11-17T04:08:45Z-
dc.date.issued2021en_US
dc.identifier.other2021eewhs354en_US
dc.identifier.urihttp://dspace.cityu.edu.hk/handle/2031/9502-
dc.description.abstractGraph partitioning, also known as clustering, has always been an important subject of data analysis and is currently applied in different fields in reality. For example, in business, cluster analysis is used to find different customer groups and distinguish the characteristics of different customer groups through purchase patterns. In biology, cluster analysis is used to classify animal and plant genes to gain insights into biological structure. The definition of clustering is grouping similar objects into the same group. This project aims to implement different clustering methods, one is based on graph neural network (GCN), and the others are based on classic clustering algorithms (K-mean, Hierarchical, etc.). In this project, the sample data will be represented in graph form and then perform clustering by Python. Then make comparisons for all clustering methods to evaluate the results based on standards such as execution time and accuracy to know the performance of different clustering methods.en_US
dc.rightsThis work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.en_US
dc.rightsAccess is restricted to CityU users.en_US
dc.titleGraph neural network for graph partitioningen_US
dc.contributor.departmentDepartment of Electrical Engineeringen_US
dc.description.supervisorSupervisor: Dr. Tang, Wallace K S; Assessor: Prof. Chen, Guanrongen_US
Appears in Collections:Electrical Engineering - Undergraduate Final Year Projects 

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