Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/2360
Title: Mining high-dimensional and graph data using spectral analysis
Authors: Li, Wenyuan
Keywords: DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Issue Date: 2007
Source: Li, W. Y. (2007). Mining high-dimensional and graph data using spectral analysis. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Although the field of data mining has seen major advancements in the past fifteen years, algorithms for handling complex data (with high dimensionality or complex graph structures) are only becoming the mainstream in recent years. To address the difficulties of mining complex data, we argue that a right understanding of data characteristics (i.e., the general information of the data that is not particularly designed for any specific data mining task, but might enhance many types of data mining tasks) is important. The objective of this thesis is to study and exploit spectral information to provide quick insights into how data characteristics are beneficial to specific applications. We study issues concerning the design of how spectral information can be integrated into the needs of different types of analysis.
URI: https://hdl.handle.net/10356/2360
DOI: 10.32657/10356/2360
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

Files in This Item:
File Description SizeFormat 
SCE-THESES_122.pdf3.58 MBAdobe PDFThumbnail
View/Open

Page view(s) 50

389
Updated on May 12, 2021

Download(s) 20

134
Updated on May 12, 2021

Google ScholarTM

Check

Altmetric


Plumx

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