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عنوان
Understanding complex datasets

پدید آورنده
/ David Skillicorn

موضوع
Data mining.,Data structures (Computer science),Computer algorithms.,Matrices.,Decomposition (Mathematics)

رده

کتابخانه
Central Library, Center of Documentation and Supply of Scientific Resources

محل استقرار
استان: East Azarbaijan ـ شهر:

Central Library, Center of Documentation and Supply of Scientific Resources

تماس با کتابخانه : 04133443834

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
9781584888321 (alk. paper)

NATIONAL BIBLIOGRAPHY NUMBER

Country Code
IR
Number
E-9507

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

COUNTRY OF PUBLICATION OR PRODUCTlON

Country of publication
IR

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Understanding complex datasets
General Material Designation
[Book]
Other Title Information
:data mining with matrix decompositions
First Statement of Responsibility
/ David Skillicorn

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Boca Raton
Name of Publisher, Distributor, etc.
: Chapman & Hall/CRC Press,
Date of Publication, Distribution, etc.
, c2007.

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
xxi, 236 p., [8] p. of plates
Other Physical Details
: ill. (some col.)
Dimensions
; 25 cm.

SERIES

Series Title
(Chapman & Hall/CRC data mining and knowledge discovery series.)

NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.

Text of Note
Print - Electronic

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes bibliographical references (p. 223-232) and index.

CONTENTS NOTE

Text of Note
1Data Mining1 --1.1What is data like?4 --1.2Data-mining techniques5 --1.2.1Prediction6 --1.2.2Clustering11 --1.2.3Finding outliers16 --1.2.4Finding local patterns16 --1.3Why use matrix decompositions?17 --1.3.1Data that comes from multiple processes18 --1.3.2Data that has multiple causes19 --1.3.3What are matrix decompositions used for?20 --2Matrix decompositions23 --2.2Interpreting decompositions28 --2.2.1Factor interpretation -- hidden sources29 --2.2.2Geometric interpretation -- hidden clusters29 --2.2.3Component interpretation -- underlying processes32 --2.2.4Graph interpretation -- hidden connections32 --2.3Applying decompositions36 --2.3.1Selecting factors, dimensions, components, or waystations36 --2.3.2Similarity and clustering41 --2.3.3Finding local relationships42 --2.3.4Sparse representations43 --2.3.5Oversampling44 --2.4Algorithm issues45 --2.4.1Algorithms and complexity45 --2.4.2Data preparation issues45 --2.4.3Updating a decomposition46 --3Singular Value Decomposition (SVD)49 --3.2Interpreting an SVD54 --3.2.1Factor interpretation54 --3.2.2Geometric interpretation56 --3.2.3Component interpretation60 --3.2.4Graph interpretation61 --3.3Applying SVD62 --3.3.1Selecting factors, dimensions, components, and waystations62 --3.3.2Similarity and clustering70 --3.3.3Finding local relationships73 --3.3.4Sampling and sparsifying by removing values76 --3.3.5Using domain knowledge or priors77 --3.4Algorithm issues77 --3.4.1Algorithms and complexity77 --3.4.2Updating an SVD78 --3.5Applications of SVD78 --3.5.1The workhorse of noise removal78 --3.5.2Information retrieval -- Latent Semantic Indexing (LSI)78 --3.5.3Ranking objects and attributes by interestingness81 --3.5.4Collaborative filtering81 --3.5.5Winnowing microarray data86 --3.6Extensions87 --3.6.1PDDP87 --3.6.2The CUR decomposition87 --4Graph Analysis91 --4.1Graphs versus datasets91 --4.2Adjacency matrix95 --4.3Eigenvalues and eigenvectors96 --4.4Connections to SVD97 --4.5Google's PageRank98 --4.6Overview of the embedding process101 --4.7Datasets versus graphs102 --4.7.1Mapping Euclidean space to an affinity matrix103 --4.7.2Mapping an affinity matrix to a representation matrix104 --4.8Eigendecompositions110 --4.9Clustering111 --4.10Edge prediction114 --4.11Graph substructures115 --4.12The ATHENS system for novel-knowledge discovery118 --4.13Bipartite graphs121 --5SemiDiscrete Decomposition (SDD)123 --5.2Interpreting an SDD132 --5.2.1Factor interpretation133 --5.2.2Geometric interpretation133 --5.2.3Component interpretation134 --5.2.4Graph interpretation134 --5.3Applying an SDD134 --5.3.1Truncation134 --5.3.2Similarity and clustering135 --5.4Algorithm issues138 --5.5Extensions139 --5.5.1Binary nonorthogonal matrix decomposition139 --6Using SVD and SDD together141 --6.1SVD then SDD142 --6.1.1Applying SDD to A[subscript k]143 --6.1.2Applying SDD to the truncated correlation matrices143 --6.2Applications of SVD and SDD together144 --6.2.1Classifying galaxies144 --6.2.2Mineral exploration145 --6.2.3Protein conformation151 --7Independent Component Analysis (ICA)155 --7.2Interpreting an ICA159 --7.2.1Factor interpretation159 --7.2.2Geometric interpretation159 --7.2.3Component interpretation106 --7.2.4Graph interpretation160 --7.3Applying an ICA160 --7.3.1Selecting dimensions160 --7.3.2Similarity and clustering161 --7.4Algorithm issues161 --7.5Applications of ICA163 --7.5.1Determining suspicious messages163 --7.5.2Removing spatial artifacts from microarrays166 --7.5.3Finding al Qaeda groups169 --8Non-Negative Matrix Factorization (NNMF)173 --8.2Interpreting an NNMF177 --8.2.1Factor interpretation177 --8.2.2Geometric interpretation177 --8.2.3Component interpretation178 --8.2.4Graph interpretation178 --8.3Applying an NNMF178 --8.3.1Selecting factors178 --8.3.2Denoising179 --8.3.3Similarity and clustering180 --8.4Algorithm issues180 --8.4.1Algorithms and complexity180 --8.4.2Updating180 --8.5Applications of NNMF181 --8.5.1Topic detection181 --8.5.2Microarray analysis181 --8.5.3Mineral exploration revisited182 --9Tensors189 --9.1The Tucker3 tensor decomposition190 --9.2The CP decomposition193 --9.3Applications of tensor decompositions194 --9.3.1Citation data194 --9.3.2Words, documents, and links195 --9.3.3Users, keywords, and time in chat rooms195 --9.4Algorithmic issues196 --Appendix AMatlab scripts203.
Text of Note
DSU Title III 2007-2012.

SERIES

Title
Chapman & Hall/CRC data mining and knowledge discovery series

TOPICAL NAME USED AS SUBJECT

Data mining.
Data structures (Computer science)
Computer algorithms.
Matrices.
Decomposition (Mathematics)

DEWEY DECIMAL CLASSIFICATION

Number
006
.
312

PERSONAL NAME - PRIMARY RESPONSIBILITY

Skillicorn, David B

ORIGINATING SOURCE

Country
ایران

ELECTRONIC LOCATION AND ACCESS

Host name
Understanding complex datasets
Access number
عادی
Compression information
عادی
Electronic name
E-9507.pdf
Bits per second
0
Contact for access assistance
ایمانی
Electronic Format Type
متن
File size
0
Record control number
E-9507
Public note
انگلیسی

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