東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Mining complex high-order datasets.

Barnathan, Michael.

Linked to FindBook

Google Book

Amazon

博客來

Mining complex high-order datasets.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mining complex high-order datasets./
Author:	Barnathan, Michael.
Description:	167 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4336.
Contained By:	Dissertation Abstracts International71-07B.
Subject:	Health Sciences, Radiology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3408691
ISBN:	9781124051659

Mining complex high-order datasets.
Barnathan, Michael.

Mining complex high-order datasets. - 167 p.

Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4336.

Thesis (Ph.D.)--Temple University, 2010.

Selection of an appropriate structure for storage and analysis of complex datasets is a vital but often overlooked decision in the design of data mining and machine learning experiments. Most present techniques impose a matrix structure on the dataset, with rows representing observations and columns representing features. While this assumption is reasonable when features are scalar and do not exhibit co-dependence, the matrix data model becomes inappropriate when dependencies between non-target features must be modeled in parallel, or when features naturally take the form of higher-order multilinear structures. Such datasets particularly abound in functional medical imaging modalities, such as fMRI, where accurate integration of both spatial and temporal information is critical. Although necessary to take full advantage of the high-order structure of these datasets and built on well-studied mathematical tools, tensor analysis methodologies have only recently entered widespread use in the data mining community and remain relatively absent from the literature within the biomedical domain. Furthermore, naive tensor approaches suffer from fundamental efficiency problems which limit their practical use in large-scale high-order mining and do not capture local neighborhoods necessary for accurate spatiotemporal analysis. To address these issues, a comprehensive framework based on wavelet analysis, tensor decomposition, and the WaveCluster algorithm is proposed for addressing the problems of preprocessing, classification, clustering, compression, feature extraction, and latent concept discovery on large-scale high-order datasets, with a particular emphasis on applications in computer-assisted diagnosis. Our framework is evaluated on a 9.3 GB fMRI motor task dataset of both high dimensionality and high order, performing favorably against traditional voxelwise and spectral methods of analysis, discovering latent concepts suggestive of subject handedness, and reducing space and time complexities by up to two orders of magnitude. Novel wavelet and tensor tools are derived in the course of this work, including a novel formulation of an r-dimensional wavelet transform in terms of elementary tensor operations and an enhanced WaveCluster algorithm capable of clustering real-valued as well as binary data. Sparseness-exploiting properties are demonstrated and variations of core algorithms for specialized tasks such as image segmentation are presented.

ISBN: 9781124051659Subjects--Topical Terms:

1019076
Health Sciences, Radiology.

Mining complex high-order datasets.
LDR:03598nam 2200337 4500 001 1399857
005 20110930095850.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124051659
035 $a (UMI)AAI3408691
035 $a AAI3408691
040 $a UMI $c UMI
100 1 $a Barnathan, Michael. $3 610739
245 1 0 $a Mining complex high-order datasets.
300 $a 167 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4336.
500 $a Adviser: Vasileios Megalooikonomou.
502 $a Thesis (Ph.D.)--Temple University, 2010.
520 $a Selection of an appropriate structure for storage and analysis of complex datasets is a vital but often overlooked decision in the design of data mining and machine learning experiments. Most present techniques impose a matrix structure on the dataset, with rows representing observations and columns representing features. While this assumption is reasonable when features are scalar and do not exhibit co-dependence, the matrix data model becomes inappropriate when dependencies between non-target features must be modeled in parallel, or when features naturally take the form of higher-order multilinear structures. Such datasets particularly abound in functional medical imaging modalities, such as fMRI, where accurate integration of both spatial and temporal information is critical. Although necessary to take full advantage of the high-order structure of these datasets and built on well-studied mathematical tools, tensor analysis methodologies have only recently entered widespread use in the data mining community and remain relatively absent from the literature within the biomedical domain. Furthermore, naive tensor approaches suffer from fundamental efficiency problems which limit their practical use in large-scale high-order mining and do not capture local neighborhoods necessary for accurate spatiotemporal analysis. To address these issues, a comprehensive framework based on wavelet analysis, tensor decomposition, and the WaveCluster algorithm is proposed for addressing the problems of preprocessing, classification, clustering, compression, feature extraction, and latent concept discovery on large-scale high-order datasets, with a particular emphasis on applications in computer-assisted diagnosis. Our framework is evaluated on a 9.3 GB fMRI motor task dataset of both high dimensionality and high order, performing favorably against traditional voxelwise and spectral methods of analysis, discovering latent concepts suggestive of subject handedness, and reducing space and time complexities by up to two orders of magnitude. Novel wavelet and tensor tools are derived in the course of this work, including a novel formulation of an r-dimensional wavelet transform in terms of elementary tensor operations and an enhanced WaveCluster algorithm capable of clustering real-valued as well as binary data. Sparseness-exploiting properties are demonstrated and variations of core algorithms for specialized tasks such as image segmentation are presented.
590 $a School code: 0225.
650 4 $a Health Sciences, Radiology. $3 1019076
650 4 $a Computer Science. $3 626642
690 $a 0574
690 $a 0984
710 2 $a Temple University. $b Computer and Information Science. $3 1065462
773 0 $t Dissertation Abstracts International $g 71-07B.
790 1 0 $a Megalooikonomou, Vasileios, $e advisor
790 1 0 $a Megalooikonomou, Vasileios $e committee member
790 1 0 $a Obradovic, Zoran $e committee member
790 1 0 $a Lakaemper, Rolf $e committee member
790 1 0 $a Mohamed, Feroze $e committee member
790 1 0 $a Faro, Scott $e committee member
790 $a 0225
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3408691