Joint clustering analysis of attribute and relationship data

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2008
Authors/Contributors
Author: Deng, Chang
Abstract
Attributes of an object contain its fundamental properties. Attribute data is the main source of clustering information. Although relationship data is an extrinsic property of objects and is at least as important as attribute data, most clustering methods process only one type of characteristic data. However, analyzing attribute and relationship data together in applications such as market segmentation, social network segmentation, and image segmentation can lead to better and more meaningful clustering. In this study, we describe a new algorithm that combines attribute and relationship data for joint clustering analysis. An experimental evaluation demonstrates the usefulness and accuracy of the proposed algorithm when applied to image segmentation.
Document
Copyright statement
Copyright is held by the author.
Permissions
The author has not granted permission for the file to be printed nor for the text to be copied and pasted. If you would like a printable copy of this thesis, please contact summit-permissions@sfu.ca.
Scholarly level
Language
English
Member of collection
Attachment Size
etd4161.pdf 12.83 MB