Understanding complex datasets
Subtitle: data mining with matrix decompositions
By: David Skillicorn.
Series: Data mining and knowledge discovery series
Publisher: CRC Press
Published: 2007
Place of publication: Boca Raton
Language: eng
ISBN-13: 9781584888321
Subjects: Data mining.; Data structures (Computer science); Computer algorithms.
Notes: Includes bibliographical references and index.
Table of contents: title: Preface
pagenum: xiii; label: 1
title: Data Mining
pagenum: 1; label: 1.1
title: What is data like?
pagenum: 4; label: 1.2
title: Data-mining techniques
pagenum: 5; label: 1.2.1
title: Prediction
pagenum: 6; label: 1.2.2
title: Clustering
pagenum: 11; label: 1.2.3
title: Finding outliers
pagenum: 16; label: 1.2.4
title: Finding local patterns
pagenum: 16; label: 1.3
title: Why use matrix decompositions?
pagenum: 17; label: 1.3.1
title: Data that comes from multiple processes
pagenum: 18; label: 1.3.2
title: Data that has multiple causes
pagenum: 19; label: 1.3.3
title: What are matrix decompositions used for?
pagenum: 20; label: 2
title: Matrix decompositions
pagenum: 23; label: 2.1
title: Definition
pagenum: 23; label: 2.2
title: Interpreting decompositions
pagenum: 28; label: 2.2.1
title: Factor interpretation - hidden sources
pagenum: 29; label: 2.2.2
title: Geometric interpretation - hidden clusters
pagenum: 29; label: 2.2.3
title: Component interpretation - underlying processes
pagenum: 32; label: 2.2.4
title: Graph interpretation - hidden connections
pagenum: 32; label: 2.2.5
title: Summary
pagenum: 34; label: 2.2.6
title: Example
pagenum: 34; label: 2.3
title: Applying decompositions
pagenum: 36; label: 2.3.1
title: Selecting factors, dimensions, components, or waystations
pagenum: 36; label: 2.3.2
title: Similarity and clustering
pagenum: 41; label: 2.3.3
title: Finding local relationships
pagenum: 42; label: 2.3.4
title: Sparse representations
pagenum: 43; label: 2.3.5
title: Oversampling
pagenum: 44; label: 2.4
title: Algorithm issues
pagenum: 45; label: 2.4.1
title: Algorithms and complexity
pagenum: 45; label: 2.4.2
title: Data preparation issues
pagenum: 45; label: 2.4.3
title: Updating a decomposition
pagenum: 46; label: 3
title: Singular Value Decomposition (SVD)
pagenum: 49; label: 3.1
title: Definition
pagenum: 49; label: 3.2
title: Interpreting an SVD
pagenum: 54; label: 3.2.1
title: Factor interpretation
pagenum: 54; label: 3.2.2
title: Geometric interpretation
pagenum: 56; label: 3.2.3
title: Component interpretation
pagenum: 60; label: 3.2.4
title: Graph interpretation
pagenum: 61; label: 3.3
title: Applying SVD
pagenum: 62; label: 3.3.1
title: Selecting factors, dimensions, components, and waystations
pagenum: 62; label: 3.3.2
title: Similarity and clustering
pagenum: 70; label: 3.3.3
title: Finding local relationships
pagenum: 73; label: 3.3.4
title: Sampling and sparsifying by removing values
pagenum: 76; label: 3.3.5
title: Using domain knowledge or priors
pagenum: 77; label: 3.4
title: Algorithm issues
pagenum: 77; label: 3.4.1
title: Algorithms and complexity
pagenum: 77; label: 3.4.2
title: Updating an SVD
pagenum: 78; label: 3.5
title: Applications of SVD
pagenum: 78; label: 3.5.1
title: The workhorse of noise removal
pagenum: 78; label: 3.5.2
title: Information retrieval - Latent Semantic Indexing (LSI)
pagenum: 78; label: 3.5.3
title: Ranking objects and attributes by interestingness
pagenum: 81; label: 3.5.4
title: Collaborative filtering
pagenum: 81; label: 3.5.5
title: Winnowing microarray data
pagenum: 86; label: 3.6
title: Extensions
pagenum: 87; label: 3.6.1
title: PDDP
pagenum: 87; label: 3.6.2
title: The CUR decomposition
pagenum: 87; label: 4
title: Graph Analysis
pagenum: 91; label: 4.1
title: Graphs versus datasets
pagenum: 91; label: 4.2
title: Adjacency matrix
pagenum: 95; label: 4.3
title: Eigenvalues and eigenvectors
pagenum: 96; label: 4.4
title: Connections to SVD
pagenum: 97; label: 4.5
title: Google's PageRank
pagenum: 98; label: 4.6
title: Overview of the embedding process
pagenum: 101; label: 4.7
title: Datasets versus graphs
pagenum: 102; label: 4.7.1
title: Mapping Euclidean space to an affinity matrix
pagenum: 103; label: 4.7.2
title: Mapping an affinity matrix to a representation matrix
pagenum: 104; label: 4.8
title: Eigendecompositions
pagenum: 110; label: 4.9
title: Clustering
pagenum: 111; label: 4.10
title: Edge prediction
pagenum: 114; label: 4.11
title: Graph substructures
pagenum: 115; label: 4.12
title: The ATHENS system for novel-knowledge discovery
pagenum: 118; label: 4.13
title: Bipartite graphs
pagenum: 121; label: 5
title: SemiDiscrete Decomposition (SDD)
pagenum: 123; label: 5.1
title: Definition
pagenum: 123; label: 5.2
title: Interpreting an SDD
pagenum: 132; label: 5.2.1
title: Factor interpretation
pagenum: 133; label: 5.2.2
title: Geometric interpretation
pagenum: 133; label: 5.2.3
title: Component interpretation
pagenum: 134; label: 5.2.4
title: Graph interpretation
pagenum: 134; label: 5.3
title: Applying an SDD
pagenum: 134; label: 5.3.1
title: Truncation
pagenum: 134; label: 5.3.2
title: Similarity and clustering
pagenum: 135; label: 5.4
title: Algorithm issues
pagenum: 138; label: 5.5
title: Extensions
pagenum: 139; label: 5.5.1
title: Binary nonorthogonal matrix decomposition
pagenum: 139; label: 6
title: Using SVD and SDD together
pagenum: 141; label: 6.1
title: SVD then SDD
pagenum: 142; label: 6.1.1
title: Applying SDD to \(A_k\)
pagenum: 143; label: 6.1.2
title: Applying SDD to the truncated correlation matrices
pagenum: 143; label: 6.2
title: Applications of SVD and SDD together
pagenum: 144; label: 6.2.1
title: Classifying galaxies
pagenum: 144; label: 6.2.2
title: Mineral exploration
pagenum: 145; label: 6.2.3
title: Protein conformation
pagenum: 151; label: 7
title: Independent Component Analysis (ICA)
pagenum: 155; label: 7.1
title: Definition
pagenum: 156; label: 7.2
title: Interpreting an ICA
pagenum: 159; label: 7.2.1
title: Factor interpretation
pagenum: 159; label: 7.2.2
title: Geometric interpretation
pagenum: 159; label: 7.2.3
title: Component interpretation
pagenum: 160; label: 7.2.4
title: Graph interpretation
pagenum: 160; label: 7.3
title: Applying an ICA
pagenum: 160; label: 7.3.1
title: Selecting dimensions
pagenum: 160; label: 7.3.2
title: Similarity and clustering
pagenum: 161; label: 7.4
title: Algorithm issues
pagenum: 161; label: 7.5
title: Applications of ICA
pagenum: 163; label: 7.5.1
title: Determining suspicious messages
pagenum: 163; label: 7.5.2
title: Removing spatial artifacts from microarrays
pagenum: 166; label: 7.5.3
title: Finding al Qaeda groups
pagenum: 169; label: 8
title: Non-Negative Matrix Factorization (NNMF)
pagenum: 173; label: 8.1
title: Definition
pagenum: 174; label: 8.2
title: Interpreting an NNMF
pagenum: 177; label: 8.2.1
title: Factor interpretation
pagenum: 177; label: 8.2.2
title: Geometric interpretation
pagenum: 177; label: 8.2.3
title: Component interpretation
pagenum: 178; label: 8.2.4
title: Graph interpretation
pagenum: 178; label: 8.3
title: Applying an NNMF
pagenum: 178; label: 8.3.1
title: Selecting factors
pagenum: 178; label: 8.3.2
title: Denoising
pagenum: 179; label: 8.3.3
title: Similarity and clustering
pagenum: 180; label: 8.4
title: Algorithm issues
pagenum: 180; label: 8.4.1
title: Algorithms and complexity
pagenum: 180; label: 8.4.2
title: Updating
pagenum: 181; label: 8.5
title: Applications of NNMF
pagenum: 181; label: 8.5.1
title: Topic detection
pagenum: 181; label: 8.5.2
title: Microarray analysis
pagenum: 182; label: 8.5.3
title: Mineral exploration revisited
pagenum: 182; label: 9
title: Tensors
pagenum: 189; label: 9.1
title: The Tucker3 tensor decomposition
pagenum: 190; label: 9.2
title: The CP decomposition
pagenum: 193; label: 9.3
title: Applications of tensor decompositions
pagenum: 194; label: 9.3.1
title: Citation data
pagenum: 194; label: 9.3.2
title: Words, documents, and links
pagenum: 195; label: 9.3.3
title: Users, keywords, and time in chat rooms
pagenum: 195; label: 9.4
title: Algorithmic issues
pagenum: 196; label: 10
title: Conclusion
pagenum: 197; label: Appendix A
title: Matlab scripts
pagenum: 203; title: Bibliography
pagenum: 223; title: Index
pagenum: 233