Mining high-dimensional and graph data using spectral analysis

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 r...

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Bibliographic Details
Main Author: Li, Wenyuan
Other Authors: Ng Wee Keong
Format: Thesis
Published: 2008
Subjects:
Online Access:https://hdl.handle.net/10356/2360
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author Li, Wenyuan
author2 Ng Wee Keong
author_facet Ng Wee Keong
Li, Wenyuan
author_sort Li, Wenyuan
collection NTU
description 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.
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spelling ntu-10356/23602023-03-04T00:39:15Z Mining high-dimensional and graph data using spectral analysis Li, Wenyuan Ng Wee Keong School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval 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. DOCTOR OF PHILOSOPHY (SCE) 2008-09-17T09:00:58Z 2008-09-17T09:00:58Z 2007 2007 Thesis Li, W. Y. (2007). Mining high-dimensional and graph data using spectral analysis. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/2360 10.32657/10356/2360 Nanyang Technological University application/pdf
spellingShingle DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Li, Wenyuan
Mining high-dimensional and graph data using spectral analysis
title Mining high-dimensional and graph data using spectral analysis
title_full Mining high-dimensional and graph data using spectral analysis
title_fullStr Mining high-dimensional and graph data using spectral analysis
title_full_unstemmed Mining high-dimensional and graph data using spectral analysis
title_short Mining high-dimensional and graph data using spectral analysis
title_sort mining high dimensional and graph data using spectral analysis
topic DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
url https://hdl.handle.net/10356/2360
work_keys_str_mv AT liwenyuan mininghighdimensionalandgraphdatausingspectralanalysis